Industrial applications of soft computing: a review
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paper_title: Fuzzy logic, neural networks, and soft computing

paper_content:

In retrospect, the year 1990 may well be viewed as the beginning of a new trend in the design of household appliances, consumer electronics, cameras, and other types of widely used consumer products. The trend in question relates to a marked increase in what might be called the Machine Intelligence Quotient (MIQ) of such products compared to what it was before 1990. Today, we have microwave ovens and washing machines that can figure out on their own what settings to use to perform their tasks optimally; cameras that come close to professional photographers in picture-taking ability; and many other products that manifest an impressive capability to reason, make intelligent decisions, and learn from experience
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paper_title: From Computing With Numbers To Computing With Words From Manipulation Of Measurements To Manipulation of Perceptions

paper_content:

Discusses a methodology for reasoning and computing with perceptions rather than measurements. An outline of such a methodology-referred to as a computational theory of perceptions is presented in this paper. The computational theory of perceptions, or CTP for short, is based on the methodology of CW. In CTP, words play the role of labels of perceptions and, more generally, perceptions are expressed as propositions in a natural language. CW-based techniques are employed to translate propositions expressed in a natural language into what is called the Generalized Constraint Language (GCL). In this language, the meaning of a proposition is expressed as a generalized constraint, N is R, where N is the constrained variable, R is the constraining relation and isr is a variable copula in which r is a variable whose value defines the way in which R constrains S. Among the basic types of constraints are: possibilistic, veristic, probabilistic, random set, Pawlak set, fuzzy graph and usuality. The wide variety of constraints in GCL makes GCL a much more expressive language than the language of predicate logic. In CW, the initial and terminal data sets, IDS and TDS, are assumed to consist of propositions expressed in a natural language. These propositions are translated, respectively, into antecedent and consequent constraints. Consequent constraints are derived from antecedent constraints through the use of rules of constraint propagation. The principal constraint propagation rule is the generalized extension principle. The derived constraints are retranslated into a natural language, yielding the terminal data set (TDS). The rules of constraint propagation in CW coincide with the rules of inference in fuzzy logic. A basic problem in CW is that of explicitation of N, R, and r in a generalized constraint, X is R, which represents the meaning of a proposition, p, in a natural language.
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paper_title: Neurocontrol and elastic fuzzy logic: capabilities, concepts, and applications

paper_content:

The author shows how elastic fuzzy logic (EFL) nets make it possible to combine the capabilities of expert systems with the learning capabilities of neural networks at a high level. ANN (artificial neural network) implementations have advantages in terms of hardware implementation, ease of use, generality, and links to the brain, which is still the only true intelligent controller available. Neurocontrol is useful in cloning experts, tracking trajectories or setpoints, and optimization (e.g., approximate dynamic programming). There has been substantial success in controlling robot arms (including the main arm of the Space Shuttle), chemical process control, continuous production of high-quality parts, and other aerospace applications. A review of the basic designs and concepts, with reference to both the applications and future research opportunities, is given. >
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paper_title: Fuzzy logic, neural networks, and soft computing

paper_content:

In retrospect, the year 1990 may well be viewed as the beginning of a new trend in the design of household appliances, consumer electronics, cameras, and other types of widely used consumer products. The trend in question relates to a marked increase in what might be called the Machine Intelligence Quotient (MIQ) of such products compared to what it was before 1990. Today, we have microwave ovens and washing machines that can figure out on their own what settings to use to perform their tasks optimally; cameras that come close to professional photographers in picture-taking ability; and many other products that manifest an impressive capability to reason, make intelligent decisions, and learn from experience
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paper_title: Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems

paper_content:

This short article shows that under some minor restrictions , the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on re presentational power, etc., can be applied to both models directly. It is of interest to observe that two models stemmi ng from different origins turn out to be functional equivale nt. This paper demonstrates the functional equivalence between radial basis function networks (RBFN’s) and a simplied class of fuzzy inference systems. Though these two models are motivated from different origins (RBFN’s from physiology and fuzzy inference systems from cognitive science), they share common characteristics not only in their operations on data, but also in their learning process to achieve desired mappings. We show that under some minor restrictions, they are functionally equivalent; the learning algorithms and the analysis on representational p ower for one model can be applied to the other, and vice versa.
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paper_title: Neurocontrol and elastic fuzzy logic: capabilities, concepts, and applications

paper_content:

The author shows how elastic fuzzy logic (EFL) nets make it possible to combine the capabilities of expert systems with the learning capabilities of neural networks at a high level. ANN (artificial neural network) implementations have advantages in terms of hardware implementation, ease of use, generality, and links to the brain, which is still the only true intelligent controller available. Neurocontrol is useful in cloning experts, tracking trajectories or setpoints, and optimization (e.g., approximate dynamic programming). There has been substantial success in controlling robot arms (including the main arm of the Space Shuttle), chemical process control, continuous production of high-quality parts, and other aerospace applications. A review of the basic designs and concepts, with reference to both the applications and future research opportunities, is given. >
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paper_title: Multiple Model Approaches to Modelling and Control

paper_content:

1. Basic Principles: The Operating Regime Approach 2. Modelling: Fuzzy Set Methods for Local Modelling Identification 3. Modelling of Electrically Stimulated Muscle 4. Process Modelling Using a Functional State Approach 5. Markov Mixtures of Experts 6. Active Learning With Mixture Models 7. Local Learning in Local Model Networks 8. Side Effects of Normalising Basic Functions 9. Control: Heterogeneous Control Laws 10. Local Laguerre Models 11. Multiple Model Adaptive Control 12. H Control Using Multiple Linear Models 13. Synthesis of Fuzzy Control Systems Based on Linear Takagi-Sugeno Fuzzy Models
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paper_title: Pattern recognition using fuzzy inference with lacked input data

paper_content:

In pattern processing, it is difficult for all features to be extracted correctly. For a system using fuzzy inference, if input datum is lacking, the system does not work well. In the paper, a framework of a modified fuzzy inference method with lack of input data is introduced, and experimental results are provided. In the proposed method that is modified from Mamdani's fuzzy inference, a result of each rule is the adjustment for the purpose of protection from influence of lacking input data. The adjustment of the resulting fuzzy labels at each rule is used as the degree of importance which is set up in the rules by humans. In the results of a simple experiment, the system can infer well with lack of input data using this method. In results of simple experiments using a set of five fuzzy rules (three input and three output), when one or two input data are lacking, the system infers correctly.
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paper_title: Foundations of distributed artificial intelligence

paper_content:

Partial table of contents: FORMULATIVE READINGS. Logical Foundations of Distributed Artificial Intelligence (E. Werner). Distributed Artificial Intelligence Testbeds (K. Decker). COOPERATION, COORDINATION, AND AGENCY. Coordination Techniques for Distributed Artificial Intelligence (N. Jennings). Negotiation Principles (H. Muller). Planning in Distributed Artificial Intelligence (E. Durfee). DAI FRAMEWORKS AND THEIR APPLICATIONS. IMAGINE: An Integrated Environment for Constructing Distributed Artificial Intelligence Systems (D. Steiner). AGenDA--A General Testbed for Distributed Artificial Intelligence Applications (K. Fischer, et al.). Agent Factory: An Environment for the Fabrication of Multiagent Systems (G. O'Hare). RELATED DISCIPLINES. Philosophy and Distributed Artificial Intelligence: The Case of Joint Intention (R. Tuomela). User Design Issues for Distributed Artificial Intelligence (L. Hall). Appendix. Index.
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paper_title: Fuzzy systems are universal approximators

paper_content:

The author proves that fuzzy systems are universal approximators. The Stone-Weierstrass theorem is used to prove that fuzzy systems with product inference, centroid defuzzification, and a Gaussian membership function are capable of approximating any real continuous function on a compact set to arbitrary accuracy. This result can be viewed as an existence theorem of an optimal fuzzy system for a wide variety of problems. >
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paper_title: Fuzzy computing for data mining

paper_content:

The study is devoted to linguistic data mining, an endeavor that exploits the concepts, constructs, and mechanisms of fuzzy set theory. The roles of information granules, information granulation, and the techniques therein are discussed in detail. Particular attention is given to the manner in which these information granules are represented as fuzzy sets and manipulated according to the main mechanisms of fuzzy sets. We introduce unsupervised learning (clustering) where optimization is supported by the linguistic granules of context, thereby giving rise to so-called context-sensitive fuzzy clustering. The combination of neuro, evolutionary, and granular computing in the context of data mining is explored. Detailed numerical experiments using well-known datasets are also included and analyzed.
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paper_title: Identification and control of dynamic systems using recurrent fuzzy neural networks

paper_content:

Proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNN-fuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.
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paper_title: Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

paper_content:

Fault diagnosis requires a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is needed which can be learned from experimental or simulated data. A fuzzy logic based diagnosis is advantageous. It allows an easy incorporation of a-priori known rules and also enables the user to understand the inference of the system. In this contribution, a new diagnosis scheme is presented and applied to a DC motor. The approach is based on a combination of structural a-priori knowledge and measured data in order to create a hierarchical diagnosis system that can be adapted to different motors. Advantages of the system are its high degree of transparency and an increased robustness.
---
paper_title: From Computing With Numbers To Computing With Words From Manipulation Of Measurements To Manipulation of Perceptions

paper_content:

Discusses a methodology for reasoning and computing with perceptions rather than measurements. An outline of such a methodology-referred to as a computational theory of perceptions is presented in this paper. The computational theory of perceptions, or CTP for short, is based on the methodology of CW. In CTP, words play the role of labels of perceptions and, more generally, perceptions are expressed as propositions in a natural language. CW-based techniques are employed to translate propositions expressed in a natural language into what is called the Generalized Constraint Language (GCL). In this language, the meaning of a proposition is expressed as a generalized constraint, N is R, where N is the constrained variable, R is the constraining relation and isr is a variable copula in which r is a variable whose value defines the way in which R constrains S. Among the basic types of constraints are: possibilistic, veristic, probabilistic, random set, Pawlak set, fuzzy graph and usuality. The wide variety of constraints in GCL makes GCL a much more expressive language than the language of predicate logic. In CW, the initial and terminal data sets, IDS and TDS, are assumed to consist of propositions expressed in a natural language. These propositions are translated, respectively, into antecedent and consequent constraints. Consequent constraints are derived from antecedent constraints through the use of rules of constraint propagation. The principal constraint propagation rule is the generalized extension principle. The derived constraints are retranslated into a natural language, yielding the terminal data set (TDS). The rules of constraint propagation in CW coincide with the rules of inference in fuzzy logic. A basic problem in CW is that of explicitation of N, R, and r in a generalized constraint, X is R, which represents the meaning of a proposition, p, in a natural language.
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paper_title: Use of fuzzy logic for modeling, estimation, and prediction in switched reluctance motor drives

paper_content:

Switched reluctance motor drives may be used in many commercial applications due to their simplicity and low cost. These drives require rotor position feedback to operate. However, in many systems, rotor position sensors have disadvantages. In this paper, a position sensorless scheme is described which uses fuzzy modeling, estimation and prediction. An important feature is that saturation and real-time nonideal effects are not ignored, but that no mathematical model is required. Instead, a fuzzy logic-based model is constructed from both static and real-time motor data, and from this model the rotor position is estimated. The system also incorporates fuzzy logic-based methods to provide a high robustness against noise. This includes a fuzzy predictive filter which combines both fuzzy logic-based time-series prediction, as well as a heuristic knowledge-based algorithm to detect and discard feedback signal error. In addition, the method uses heuristic knowledge to choose the most desirable phase for angle estimation in order to minimize the effect of feedback error. It is also shown that, by using fuzzy logic, the estimation scheme offers a high robustness and reliability and is thus well suited to a wide range of systems.
---
paper_title: Neurocontrol and elastic fuzzy logic: capabilities, concepts, and applications

paper_content:

The author shows how elastic fuzzy logic (EFL) nets make it possible to combine the capabilities of expert systems with the learning capabilities of neural networks at a high level. ANN (artificial neural network) implementations have advantages in terms of hardware implementation, ease of use, generality, and links to the brain, which is still the only true intelligent controller available. Neurocontrol is useful in cloning experts, tracking trajectories or setpoints, and optimization (e.g., approximate dynamic programming). There has been substantial success in controlling robot arms (including the main arm of the Space Shuttle), chemical process control, continuous production of high-quality parts, and other aerospace applications. A review of the basic designs and concepts, with reference to both the applications and future research opportunities, is given. >
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paper_title: Electric Power Applications of Fuzzy Systems

paper_content:

From the Publisher: ::: This book offers an introduction to applications of fuzzy system theory to selected areas of electric power engineering. It presents theoretical background material from a practical point of view and then explores a number of applications of fuzzy systems. Most recently, there has been a tremendous surge in research and application articles on this subject. Until now though, there have been no books that put together a practical guide to the fundamentals and applications aspects. ELECTRIC POWER APPLICATIONS OF FUZZY SYSTEMS presents, under one cover, original contributions by authors who have pioneered in the application of fuzzy system theory to the electric power engineering field. Each chapter contains both an introduction to and a state-of-the-art review of each application area.Sponsored by:IEEE Power Engineering Society.
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paper_title: Dynamic formation on mobile agents and its evolutionary parallel computation

paper_content:

Proposes a chaotic evolutionary computation algorithm instead of a conventional genetic algorithm for such intelligent agents as welfare robots which assist humans. This evolutionary computation is realized by applying chaotic retrieval and soft DNA (soft computing oriented data driven functional scheduling architecture) on associative memories. We apply this evolutionary computation to multi-agent robots which move abreast and intelligent transport systems. Essentially, the process of this evolutionary computation is parallel processing. Therefore, we implement its parallel processing algorithm on A-NET (actors network) parallel object-oriented computer, and show the usefulness of parallel processing for the proposed evolutionary computation.
---
paper_title: Fuzzy computing for data mining

paper_content:

The study is devoted to linguistic data mining, an endeavor that exploits the concepts, constructs, and mechanisms of fuzzy set theory. The roles of information granules, information granulation, and the techniques therein are discussed in detail. Particular attention is given to the manner in which these information granules are represented as fuzzy sets and manipulated according to the main mechanisms of fuzzy sets. We introduce unsupervised learning (clustering) where optimization is supported by the linguistic granules of context, thereby giving rise to so-called context-sensitive fuzzy clustering. The combination of neuro, evolutionary, and granular computing in the context of data mining is explored. Detailed numerical experiments using well-known datasets are also included and analyzed.
---
paper_title: Computational intelligence and soft computing for space applications

paper_content:

Systems using computational intelligence and soft computing have been successfully developed for many industrial and space applications. These systems seek to emulate the type of reasoning that humans perform when solving complex tasks. The field of soft computing, as defined by Zadeh-the inventor of fuzzy logic-encompasses fuzzy logic as well as other methodologies such as neural networks, genetic algorithms, and uncertainty management. It is expected that soft-computing techniques will eventually become as common and prevalent as traditional methods of computer science. This paper presents an overview of applications of fuzzy logic and soft computing to space projects. The role of fuzzy systems that can learn from experience to improve their performance is discussed. We present a report on applications of these adaptive systems to NASA space projects such as the orbital operations of the Space Shuttle, which include attitude control and rendezvous/docking operations. We also provide insights on the future of computational intelligence and soft computing and of their vast potential in industrial applications.
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paper_title: Applications of neural networks to home appliances

paper_content:

We are now entering the generation where there is more concern for the quality of life rather than mere material reward. So home appliances should be supplied to realize the higher quality of life. In this trend, neural network is one key technology toward this. As examples of the applications of neural networks the intelligent washing machine, the intelligent rice cooker and the intelligent air-conditioner are introduced. And what is going on the fundamental research toward intelligent home appliances are introduced.
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paper_title: Data strip mining for the virtual design of pharmaceuticals with neural networks

paper_content:

A novel neural network based technique, called "data strip mining" extracts predictive models from data sets which have a large number of potential inputs and comparatively few data points. This methodology uses neural network sensitivity analysis to determine which predictors are most significant in the problem. Neural network sensitivity analysis holds all but one input to a trained neural network constant while varying each input over its entire range to determine its effect on the output. Elimination of variables through neural network sensitivity analysis and predicting performance through model cross-validation allows the analyst to reduce the number of inputs and improve the model's predictive ability at the same time. This paper demonstrates its effectiveness on a pair of problems from combinatorial chemistry with over 400 potential inputs each. For these data sets, model selection by neural sensitivity analysis outperformed other variable selection methods including the forward selection and genetic algorithm.
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paper_title: Evolving pet robot with emotional model

paper_content:

Deals with a pet robot with an emotional model. The robot requires several capabilities, such as perceiving, acting, communicating and surviving. Furthermore, it should learn various behaviors through interaction with its owner. This paper focuses on teaching a pet robot tricks or to dance. Basically, the owner can teach these tricks by simple communication based on trial and error. The robot performs the tricks by using a fuzzy controller, and further acquires tricks by a delta rule for online learning and a genetic algorithm for off-line learning. We use "Rag Warrior" as our pet robot. Experimental results show that this robot performs tricks through interaction with its owner.
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paper_title: Decoupled stator-flux-oriented induction motor drive with fuzzy neural network uncertainty observer

paper_content:

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results.
---
paper_title: Computational intelligence and soft computing for space applications

paper_content:

Systems using computational intelligence and soft computing have been successfully developed for many industrial and space applications. These systems seek to emulate the type of reasoning that humans perform when solving complex tasks. The field of soft computing, as defined by Zadeh-the inventor of fuzzy logic-encompasses fuzzy logic as well as other methodologies such as neural networks, genetic algorithms, and uncertainty management. It is expected that soft-computing techniques will eventually become as common and prevalent as traditional methods of computer science. This paper presents an overview of applications of fuzzy logic and soft computing to space projects. The role of fuzzy systems that can learn from experience to improve their performance is discussed. We present a report on applications of these adaptive systems to NASA space projects such as the orbital operations of the Space Shuttle, which include attitude control and rendezvous/docking operations. We also provide insights on the future of computational intelligence and soft computing and of their vast potential in industrial applications.
---
paper_title: Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

paper_content:

Fault diagnosis requires a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is needed which can be learned from experimental or simulated data. A fuzzy logic based diagnosis is advantageous. It allows an easy incorporation of a-priori known rules and also enables the user to understand the inference of the system. In this contribution, a new diagnosis scheme is presented and applied to a DC motor. The approach is based on a combination of structural a-priori knowledge and measured data in order to create a hierarchical diagnosis system that can be adapted to different motors. Advantages of the system are its high degree of transparency and an increased robustness.
---
paper_title: A simple direct-torque neuro-fuzzy control of PWM-inverter-fed induction motor drive

paper_content:

In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor TMS 320C31-based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterized by very fast torque and flux response, very-low-speed operation, and simple tuning capability.
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paper_title: Neural and fuzzy reconstructors for the virtual flight data recorder

paper_content:

The results are presented of a comparative study evaluating the performance of neural network (NN) and fuzzy logic reconstructors (FLRs) for the development of a virtual flight data recorder (VFDK). Typical flight data recorders (FDRS) on commercial airliners do not record the aircraft control surface deflections. These dynamic parameters are critical in the investigation of an accident or an uncommanded maneuver. The results are shown relative to a VFDR based on a neural network simulator (NNS) along with a neural network reconstructor (NNR) or a FLR The NNS is trained off-line, using available flight data for the particular aircraft, for the purpose of simulating any desired dynamic output recorded in current FDRs. The NNS is then interfaced with the NNR or with the FLR. The output of the two reconstructors are the control surface deflections which minimize a performance index based on the differences between the available data from the FDR and the output from the NNS. The study tested with night data from a B737-300 shows that both schemes, the one with the NNR and the one with the FLR, provide accurate reconstructions of the control surface deflections time histories.
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paper_title: Integrating genetic algorithms and interactive simulations for airbase logistics planning

paper_content:

There is a need for a modeling approach that can integrates simulation with human interaction and optimization to exploit the analyst’s ability in addressing difficult-to-quantify issues while utilizing computer models and algorithms to perform complex computations in systems analysis. In this paper, we present an approach that integrates human interaction with simulations and genetic algorithms for the repair-time analysis problem in airbase logistics. We present the integrative approach along with the computer system implementation, and evaluation in the context of a realistic repair time analysis problem.
---
paper_title: Computational intelligence and soft computing for space applications

paper_content:

Systems using computational intelligence and soft computing have been successfully developed for many industrial and space applications. These systems seek to emulate the type of reasoning that humans perform when solving complex tasks. The field of soft computing, as defined by Zadeh-the inventor of fuzzy logic-encompasses fuzzy logic as well as other methodologies such as neural networks, genetic algorithms, and uncertainty management. It is expected that soft-computing techniques will eventually become as common and prevalent as traditional methods of computer science. This paper presents an overview of applications of fuzzy logic and soft computing to space projects. The role of fuzzy systems that can learn from experience to improve their performance is discussed. We present a report on applications of these adaptive systems to NASA space projects such as the orbital operations of the Space Shuttle, which include attitude control and rendezvous/docking operations. We also provide insights on the future of computational intelligence and soft computing and of their vast potential in industrial applications.
---
paper_title: Fuzzy reactive piloting for continuous driving of long range autonomous planetary micro-rovers

paper_content:

A complete piloting control subsystem for a highly autonomous long range rover will be defined in order to identify the key control functions needed to achieve continuous driving. This capability can maximize range and number of interesting scientific sites visited during the limited life time of a planetary rover. To achieve continuous driving, a complete set of techniques have been employed: fuzzy based control, real-time artificial intelligence reasoning, fast and robust rover position estimation based on odometry and angular rate sensing, efficient stereo vision elevation maps based on grids, and fast reaction and planning for obstacle detection and obstacle avoidance based on a simple IF-THEN expert system with fuzzy reasoning. To quickly design and implement these techniques, graphical programming has been used to build a fully autonomous piloting system using just the techniques of classic control concepts of cyclic data processing and event driven reaction. Experimental results using the JPL rover Rocky 7 are given in order to validate the mentioned techniques for continuous driving.
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paper_title: Fuzzy implementation of a Bayesian equaliser in the presence of intersymbol and co-channel interference

paper_content:

The authors investigate the problem of channel equalisation in the presence of co-channel interference (CCI), intersymbol interference and additive white gaussian noise. The optimal Bayesian decision feedback equaliser decision function for this problem is derived and an elegant fuzzy implementation of the optimal solution is proposed. This fuzzy implemented equaliser is able to provide performance close to the optimal equaliser with a substantial reduction in computational complexity. The equaliser consists of a fuzzy equaliser with an input processing block for co-channel compensation. This preprocessor can be used under severe-to-moderate CCI and can be removed under low CCI conditions. Simulation studies demonstrate the performance of the fuzzy equaliser developed.
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paper_title: A fast and efficient lossless data-compression method

paper_content:

This paper describes an online lossless data-compression method using adaptive arithmetic coding. To achieve good compression efficiency, we employ an adaptive fuzzy-tuning modeler that applies fuzzy inference to deal efficiently with the problem of conditional probability estimation. In comparison with other lossless coding schemes, the compression results of the proposed method are good and satisfactory for various types of source data, Since we adopt the table-lookup approach for the fuzzy-tuning modeler, the design is simple, fast, and suitable for VLSI implementation.
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paper_title: The role of synchronization in digital communications using chaos. I . Fundamentals of digital communications

paper_content:

A composite material which has a substantially reduced incidence of defects after drawing, and a method of producing it. The composite material is comprised of at least one layer of plain carbon steel containing up to 1.5% of carbide former present in a carbide former-to-carbon ratio of from about 1.5 to about 25 and at least one layer of stainless steel. It is formed by pressure bonding carbon steel containing carbide former to stainless steel.
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paper_title: Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences

paper_content:

Sources of real-time traffic are generally highly unpredictable with respect to the instantaneous and average load which they create. Yet such sources will provide a significant portion of traffic in future networks, and will significantly affect the overall performance of and quality of service. Clearly high levels of compression are desirable as long as video quality remains satisfactory, and our research addresses this key issue with a novel learning-based approach. We propose the use of neural networks (NNs) as post-processors for any existing video compression scheme. The approach is to interpolate video sequences and compensate for frames which may have been lost or deliberately dropped. We show that deliberately dropping frames will significantly reduce the amount of offered traffic in the network, and hence the cell loss probability and network congestion, while the NN post-processor will preserve most of the desired video quality. Dropping frames at the sender or in the network is also a fast way to react to network overload and reduce congestion. Our interpolation techniques at the receiver, including neural network-based algorithms, provide output frame rates which are identical to (or possibly higher than) the original video sequence's frame rate. The resulting video quality is essentially equivalent to the sequence without frame drops, despite the loss of a significant fraction of the frames. Experimental evaluation using real video sequences is provided or interpolation with a connectionist NN using the backpropagation learning algorithm, the random NN (RNN) in a feed-forward configuration with its associated learning algorithm, and cubic spline interpolation. The experiments show that when more frames are being dropped or lost, the RNN performs generally better than the other techniques in terms of resulting video quality and overall performance. When the fraction of dropped frames is small, cubic splines offer better performance. Experimental data shows that this receiver-reconstructed subsampling technique significantly reduces the cell loss rates in an asynchronous transfer mode switch for different buffer sizes and service rates.
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paper_title: A genetic algorithm approach to solve channel assignment problem in cellular radio networks

paper_content:

We propose an efficient algorithm for channel assignment problem in cellular radio networks. The task is to assign channels to different cells in a spectrum-efficient way i.e. the minimum frequency band is used and at the same time interference among calls from different cells is avoided. The simplest version of this problem, where only co-channel interference is considered, is NP-complete. We propose a solution based on genetic algorithm approach. The contribution of this paper is twofold. (1) A novel way of generating the initial population which creates near optimum solutions when the population is large is presented. In all the cases we tried the best of these solutions are better or as good as those obtained by existing methods. (2) A genetic mutation operation is defined which improves the quality of the previously obtained initial solutions to an optimum solution. Simulation results are reported.
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paper_title: Power Prediction in Mobile Communication Systems Using an Optimal Neural-Network Structure

paper_content:

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required
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paper_title: Fuzzy logic applications: Technological and strategic issues

paper_content:

It is noted that fuzzy logic has not only significantly enhanced knowledge-based and expert system technology, but has fundamentally altered the granularity of intelligence. With the help of fuzzy logic, manufacturers of home appliances are today embedding intelligence inside individual products. The application of fussy logic has also transformed industrial process control and enabled new product development strategies. A review of the evolution of fuzzy logic technology aimed at an audience of business and industrial managers, is presented. The strategic business impact of the technology is analyzed. >
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paper_title: History and recent trends in soft computing: research and application aspects in Japan

paper_content:

Japanese industrial research and applications in the field of soft computing that covers fuzzy logic, neurocomputing and chaos have been surveyed. In the middle of the 1980's in Japan fuzzy technology became a central issue for mainly process control and the year 1990 became a so called "fuzzy-home electronics year." These technologies are mainly based on if-then rule based fuzzy inference with instrumentation (i.e., sensor and actuator) engineering. Then neural network technology merged with fuzzy technology in 1991 and again many consumer products were sent to the real market in Japan. Such neuro-fuzzy technologies are classified into nine categories. In 1993 chaos technologies were included in research and development of such high-tech issues. Very recently other technologies such as chaos, genetic algorithms and artificial life have been investigated by company engineers in Japan. These kinds of practical, technological aspects in Japan are discussed and the future trends are also indicated by giving many examples.
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paper_title: Application of evolutionary computations at LG Electronics

paper_content:

In this paper, we will consider a number of applications of evolutionary computations (ECs) developed at LG Electronics during the last several years. We first briefly review the basic procedure of ECs and then categorize the applications into two groups, according to the ways that ECs are used. Finally, we will discuss the details of how to get the benefits from ECs through a couple of representative examples.
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paper_title: An application of fuzzy logic to control the refrigerant distribution for the multi-type air conditioner

paper_content:

Single type air conditioners (STAC) consist of one outdoor unit and one indoor unit. Multi-type air conditioner (MTAC) consist of one compressor in outdoor unit and some indoor units. In this paper, we use the free joint MTAC which can joint six indoor units to one outdoor unit. In the control of the refrigerant distribution, there are three procedures: to measure the pipe pressure of indoor unit; to calculate the amount of the refrigerant for each indoor units accurately, and to analyze the relation between the room temperature and outdoor temperature of the system. In the conventional refrigerant distribution control, the cooling load are estimated by using a experimental relation between evaporating coil target temperature and present temperature. In this paper, we apply the fuzzy logic control method to the linear valve control for the stable refrigerant distribution in the presence of operating condition change.
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paper_title: Applications of neural networks to home appliances

paper_content:

We are now entering the generation where there is more concern for the quality of life rather than mere material reward. So home appliances should be supplied to realize the higher quality of life. In this trend, neural network is one key technology toward this. As examples of the applications of neural networks the intelligent washing machine, the intelligent rice cooker and the intelligent air-conditioner are introduced. And what is going on the fundamental research toward intelligent home appliances are introduced.
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paper_title: Refrigerator temperature control using fuzzy logic and neural network

paper_content:

This paper describes the quick and precise control method for domestic refrigerators. The proposed controller is based on the fuzzy logic and neural network designed maintaining the constant inner temperature of the refrigerator and improving the refrigerator efficiency. The fuzzy logic controller is used to maintain the inner temperature in spite of the environmental variations such as the outer temperature change or the volume change of the stored foods of the refrigerator. A neural network is used and trained to recognize the user-pattern such as the number of door openings of the refrigerator. The 1st order approximated model of the refrigerator is used for the simulation. Through the simulation in the case of the outer temperature change like summer or winter, the proposed fuzzy logic controller with a neural network is shown to be more efficient than the conventional on-off controller.
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paper_title: Recent applications of fuzzy logic to home appliances

paper_content:

Some applications of fuzzy logic to home electric appliances are introduced. Many kinds of fuzzy-controlled appliances have been produced in Japan. Fuzzy controllers have the advantage that qualitative knowledge of operations by experts can be used. However, it is difficult for even experts to design fuzzy controllers with their heuristic way. In order to overcome this problem, we developed a self-tuning method of fuzzy if-then rules using a learning algorithm of neural networks, named "Neuro-Fuzzy." We introduce two applications of "Neuro-Fuzzy" to a refrigerator and a welding machine. Another important application field of fuzzy logic is image understanding. We are trying to install "eyes" in air-conditioning systems. In order to produce comfortable thermal environment, the number and positions of occupants in a room have to be identified. We introduce a segmentation and locating method of occupants from thermal images using a fuzzy clustering algorithm and "Neuro-Fuzzy." Room temperature and wind direction can be controlled according to the identified information. >
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paper_title: Fusion of soft computing and hard computing techniques: a review of applications

paper_content:

Soft computing (SC) is an emerging collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it uses intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent ambiguity in human thinking with the real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other but synergistic and complementary instead, as emphasized by Dr. Zadeh . Considering the available literature, it is easy to conclude that the fusion of individual soft computing methodologies has been advantageous in numerous applications. In this paper, we give a review of applications where the fusion of soft computing and hard computing has provided innovative solutions for demanding real-world problems. A representative list of references is provided with evaluative discussions and conclusions.
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paper_title: Fuzzy intelligent information processing in home appliances

paper_content:

We report an application of fuzzy logic to intelligent information processing. We have automated keyword extraction from a large amount of text information on teletext programs or Internet to provide users with a summary. With a part of this technique, we have also produced television sets which can summarize teletext news programs (in Japanese) into topics and display them for the users.
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paper_title: Application of evolutionary computations at LG Electronics

paper_content:

In this paper, we will consider a number of applications of evolutionary computations (ECs) developed at LG Electronics during the last several years. We first briefly review the basic procedure of ECs and then categorize the applications into two groups, according to the ways that ECs are used. Finally, we will discuss the details of how to get the benefits from ECs through a couple of representative examples.
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paper_title: Non-linear technologies in a dishwasher

paper_content:

We demonstrate a dishwasher with two nonlinear technologies: fuzzy and chaos. Fuzzy inference is applied to determination of washing time, which prevents excessive washing and waste of electric power. The chaos technology is applied to a design of a 2 link nozzle. Chaotic behavior of the 2 link nozzle enables increased washing efficiency. >
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paper_title: Applications of neural networks to home appliances

paper_content:

We are now entering the generation where there is more concern for the quality of life rather than mere material reward. So home appliances should be supplied to realize the higher quality of life. In this trend, neural network is one key technology toward this. As examples of the applications of neural networks the intelligent washing machine, the intelligent rice cooker and the intelligent air-conditioner are introduced. And what is going on the fundamental research toward intelligent home appliances are introduced.
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paper_title: Application of evolutionary computations at LG Electronics

paper_content:

In this paper, we will consider a number of applications of evolutionary computations (ECs) developed at LG Electronics during the last several years. We first briefly review the basic procedure of ECs and then categorize the applications into two groups, according to the ways that ECs are used. Finally, we will discuss the details of how to get the benefits from ECs through a couple of representative examples.
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paper_title: Applications of neural networks to home appliances

paper_content:

We are now entering the generation where there is more concern for the quality of life rather than mere material reward. So home appliances should be supplied to realize the higher quality of life. In this trend, neural network is one key technology toward this. As examples of the applications of neural networks the intelligent washing machine, the intelligent rice cooker and the intelligent air-conditioner are introduced. And what is going on the fundamental research toward intelligent home appliances are introduced.
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paper_title: Radial basis function (RBF) network adaptive power system stabilizer

paper_content:

Summary form only given as follows. This paper presents a new approach for real-time tuning the parameters of a conventional power system stabilizer (PSS) using a radial basis function (RBF) network. The RBF network is trained using an orthogonal least squares (OLS) learning algorithm. Investigations reveal that the required number of RBF centers depends on spread factor, /spl beta/ and the number of training patterns. Studies show that a parsimonious RBF network can be obtained by presenting a relatively smaller number of training patterns, generated randomly and spreadover the entire operating domain. Investigations reveal that the dynamic performance of the system with an RBF network adaptive PSS (RBFAPSS) is virtually identical to that of an artificial neural network based adaptive PSS (ANNAPSS). The dynamic performance of the system with RBFAPSS is quite robust over a wide range of loading conditions and equivalent reactance X/sub c/.
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paper_title: A synchronous generator fuzzy excitation controller optimally designed with a genetic algorithm

paper_content:

Design of a fuzzy logic power system controller with satisfactory performance is not an easy task. The difficulties come from two aspects. First, design of a fuzzy logic computer mainly uses the experience of the human experts. To acquire enough heuristic knowledge from the domain experts and to represent this kind of knowledge appropriately with a set of fuzzy rules presents difficulties. Second, it is difficult to appropriately tune the parameters used in the fuzzy logic controller. These parameters are commonly determined by a "trial and error" method which is rather time consuming. In this paper, a genetic algorithm is introduced to design an optimal fuzzy logic controller. The proposed method has been used to design an optimal fuzzy logic excitation controller for a generating unit. Test results with the fuzzy logic controller show very satisfactory results.
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paper_title: Adaptive-edge search for power plant start-up scheduling

paper_content:

Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine-rotor stresses. A shorter start-up time not only reduces fuel and electricity consumption during the start-up process, but also increases its capability of adapting to changes in electricity demand. The start-up scheduling problem can be formulated as a function optimization problem with constraints. We have constructed an efficient and robust search model-a genetic algorithm (GA) with an enforcement operation-which forces the search along the edge of the feasible space, where the optimal schedule is supposed to exist. However, this model has to perform a prior Monte Carlo test to obtain the enforcement gains used for the implementation of the enforcement operation. In this paper, we attempt to eliminate the Monte Carlo test by proposing a self-reliant search model by introducing a GA with an adaptive enforcement operation that can generate and adapt enforcement gains during the search process. The test results of this proposed model show that the overall number of time-consuming dynamic simulations for the constraints calculation can be reduced further, thus increasing the overall efficiency of finding the optimal or near-optimal schedules.
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paper_title: Simultaneous tuning of power system damping controllers using genetic algorithms

paper_content:

This paper presents a method that simultaneously tune multiple power system damping controllers using genetic algorithms (GAs). Damping controller structures are assumed to be fixed consisting basically of lead-lag filters. The tuning method takes robustness into consideration as it guarantees system stabilization over a prespecified set of operating conditions. Modified GA operators are used in the simultaneous optimization of both phase compensations and gain settings for the stabilizers. The method is applied for global PSS tuning to the well-known New England system and to the coordinated tuning of 22 PSSs in a 1762-bus modified equivalent South-Southeastern Brazilian system.
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paper_title: Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements

paper_content:

The ability to rapidly acquire synchronized phasor measurements from around a power system opens up new possibilities for power system protection and control. In this paper, the authors develop a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements to provide fast transient stability swings prediction for use with high-speed control. Primary features of the method include constructing a fuzzy neural network for all fault locations, using a short window of realistic-precision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.
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paper_title: Neural network based demand forecasting in a deregulated environment

paper_content:

The traditional approach to load forecasting is based on processing time series of load and weather factors recorded in the past. In the dynamic environment of the deregulated power industry, historical load data may not always be available. This paper explores the possibility of an alternative approach towards load forecasting based on indirect demand estimation from available customer data. This approach requires utilization of demand models for different customer categories. This paper presents a neural network based method of demand modeling. Neural networks are designed and trained based on the aggregate demands of the groups of surveyed customers of different categories. The performance of such models depends on the neural network design and representativeness of the training data. The forecast accuracy is also affected by the forecasted group size, customer characteristics, customer classification system, and the extent of demand survey. This paper discusses the issues of neural network design and illustrates the proposed method by its application to forecasting demand of residential customers.
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paper_title: Failure diagnosis by trajectory parallel measure on attractor

paper_content:

The study of chaos is attracting attention, and a wide range of academic fields is actively involved. On the other hand, Aihara (1992) proposed the term "chaos engineering" to describe the application of chaos theory for engineering purposes, and its possibilities have been demonstrated. The approaches to the engineering applications of chaos are generally classified, into two groups: (1) creative methods and (2) analytical methods. In the former, chaos conditions, consistent with the particular application, are produced in any case, and this behavior is used in a positive manner. In the latter, a phenomenon is examined to see whether it is chaotic or not, and if it is a deterministic chaos, the latent rules involved are extracted and used for a practical purpose. Applications based on the creative methods include chaos computing, chaotic memory, chaos cipher communications, chaos art, applications using chaos fluctuations, chaos circuits, non-linear engineering systems, etc. applications using the analytical methods include failure diagnosis, deterministic non-linear short-term forecasting, identifying chaos, modeling bio-chaos, etc. This paper first describes approaches of chaotic diagnosis. Next, the trajectory parallel measure method for diagnosis is described. Finally, fault diagnosis is discussed as a prospective industrial application using practical examples.
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paper_title: Evolutionary-programming-based algorithm for environmentally-constrained economic dispatch

paper_content:

This paper develops an efficient and reliable evolutionary-programming-based algorithm for solving the environmentally constrained economic dispatch (ECED) problem. The algorithm can deal with load demand specifications in multiple intervals of the generation scheduling horizon. In the paper, the principal components of the evolutionary-programming-based ECED algorithm are presented. Solution acceleration techniques in the algorithm which enhance the speed and robustness of the algorithm are developed. The power and usefulness of the algorithm is demonstrated through its application to a test system.
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paper_title: Decentralised coordination of power system operation using dual evolutionary programming

paper_content:

This paper proposes a decentralised coordination framework for operating power systems with dispersed generation and energy storage. It then develops a novel decentralised approach for calculating the system optimal operation of resources with intertemporal links. The method combines elements of dynamic programming with evolutionary programming. Each power system resource evolves a 'future benefit' function that describes the impact of its own possible decisions on future power system operation. This 'dual evolutionary programming' approach can handle complex resource models and objective functions. It is shown to be computationally faster than discrete dynamic programming for a number of power system problems with multiple storage resources.
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paper_title: Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method

paper_content:

Conventional artificial neural network (ANN) based short-term load forecasting techniques have limitations in their use on holidays. This is due to dissimilar load behaviors of holidays compared with those of ordinary weekdays during the year and to insufficiency of training patterns. The purpose of this paper is to propose a new short-term load forecasting method for special days in anomalous load conditions. These days include public holidays, consecutive holidays, and days preceding and following holidays. The proposed method uses a hybrid approach of ANN based technique and fuzzy inference method to forecast the hourly loads of special days. In this method, special days are classified into five different day-types. Five ANN models for each day-type are used to forecast the scaled load curves of special days, and two fuzzy inference models are used to forecast the maximum and the minimum loads of special days. Finally, the results of the ANN and the fuzzy inference models are combined to forecast the 24 hourly loads of special days. The proposed method was tested with actual load data of special days for the years of 1996-1997. The test results showed very accurate forecasting with the average percentage relative error of 1.78%.
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paper_title: Transmission network expansion planning under an improved genetic algorithm

paper_content:

This paper describes the application of an improved genetic algorithm (IGA) to deal with the solution of the transmission network expansion planning (TNEP) problem. Genetic algorithms (GAs) have demonstrated the ability to deal with nonconvex, nonlinear, integer-mixed optimization problems, like the TNEP problem, better than a number of mathematical methodologies. Some special features have been added to the basic genetic algorithm (GA) to improve its performance in solving the TNEP problem for three real-life, large-scale transmission systems. Results obtained reveal that GAs represent a promising approach for dealing with such a problem. In this paper, the theoretical issues of GA applied to this problem are emphasized.
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paper_title: An interactive fuzzy-norm satisfying method for multi-objective reactive power sources planning

paper_content:

An interactive fuzzy-norm (FN) satisfying method for multi-objective reactive power sources planning is presented, by considering that the decision-maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. After determining the membership functions to solve a candidate for the satisfying solution, which is also a noninferior solution, if the DM specifies the reference membership values, the FN approach is applied for generating a corresponding global noninferior solution for the DM's goals. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values. In this way the satisfying solution for the DM can be obtained. Results of the application of the proposed method are also presented.
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paper_title: A profit-based unit commitment GA for the competitive environment

paper_content:

As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement, previously utilized to schedule generation units in a manner that minimizes costs while meeting all demand, the unit commitment (UC) algorithm must be updated. A UC algorithm that maximizes profit will play an essential role in developing successful bidding strategies for the competitive generator. Simply bidding to win contracts is insufficient; bidding strategies must result in contracts that, on average, cover the total generation costs. No longer guaranteed to be the only electricity supplier, a generation company's share of the demand will be more difficult to predict than in the past. Removing the obligation to serve softens the demand constraint. In this paper the authors provide a price/profit-based UC formulation which considers the softer demand constraint and allocates fixed and transitional costs to the scheduled hours. The authors describe a genetic algorithm solution to this new UC problem and present results for an illustrative example.
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paper_title: Integrating fuzzy control of the dexterous National Taiwan University (NTU) hand

paper_content:

Compared to traditional tendon-driven robots, the mechanism design of the National Taiwan University hand has an uncoupled configuration and is compact in size. In this paper, a specially designed compact control system, which is embedded into the NTU hand to satisfy the limited space, is developed to perform the control of the five-finger robot with 17 degrees of freedom (DOF). There are total 17 actuators including transmission mechanism, 17 potentiometers, and 18 tactile sensor pads to be integrated. Those 17 actuators should be controlled simultaneously by integrating 17 position sensing signals and 18 tactile signals. Multi-loop position control is further complicated by multi-loop force control as well as sensor integration. The proposed control system distributes the computation load into several modules and utilizes the DSP chip. It takes advantage of the fuzzy control by introducing the knowledge of human and sensor fusion with the finger joint and tactility. Using the communication function of the control system, the knowledge bases can be loaded for high level computation and modified during run time.
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paper_title: Position/force control of robot manipulators for geometrically unknown objects using fuzzy neural networks

paper_content:

In order to carry out the tasks of grinding, deburring, polishing or wiping, the end-effector of the robot manipulator has to follow the contour of an object. In this paper, the authors propose a fuzzy vector method, which enables the controller to deal efficiently with force sensor signals which include noise and/or unknown vibrations caused by the working tool, to search the direction of the constraint surface of an unknown object.
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paper_title: General guiding model for mobile robots and its complexity reduced neuro-fuzzy approximation

paper_content:

The development of techniques for autonomous mobile robot navigation has been in focus for several decades . The main objectives of this paper are twofold. One is to extend the potential based guiding (PBG) model to a more general form that can be approximated by a common type neuro-fuzzy algorithm. The extended model eliminates the strongly alternating behavior of PBG. The second is to propose a computation complexity reduction method for the general form of the neuro-fuzzy technique. Same examples are given to show the effectiveness of the extended guiding model.
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paper_title: Immunoid: an architecture for behavior arbitration based on the immune networks

paper_content:

Conventional artificial intelligent (AI) systems have been criticized for their brittleness under hostile/dynamic changing environments. Therefore, much attention has been focused on the reactive planning systems such as behavior-based AI. However, in the behavior-based AI approaches, how to construct a mechanism that realizes adequate arbitration among competence modules is still on open question. In this paper, we propose a new decentralized consensus-making system inspired from the biological immune system. And we apply our proposed method to a behavior arbitration of an autonomous mobile robot as a practical example. To verify the feasibility of our method, we carry out some simulations. In addition, we propose an adaptation mechanism that can be used to construct a suitable immune network for adequate action selection.
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paper_title: Dynamics of multi-agent systems by RasID learning

paper_content:

Presents a model and learning method for controlling multi-agent systems that are composed of plural agents. Generally speaking multi-agent systems are recognized as the systems where the group behavior of many autonomous subjects or individuals is different from the one obtained by just accumulating the behavior of each agent. In the paper, the dynamic of each agent is described by the equation including parameters for controlling the interaction of each agent, and these parameters are trained to meet the specification of the multi-agent dynamics. The dynamics of the agents can be represented by the distance between the agents and the following or dispersal parameters that express the interaction among the agents. These parameters are calculated by fuzzy inference whose antecedent part is the function of the distance between the agents. By training means and variances of the membership function of the antecedent part and consequent part, we can obtain the required dynamics of the agents. The criterion of this learning is a maximum Lyapunov exponent because of it's suitability for describing stable or chaotic movements.
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paper_title: Dynamic formation on mobile agents and its evolutionary parallel computation

paper_content:

Proposes a chaotic evolutionary computation algorithm instead of a conventional genetic algorithm for such intelligent agents as welfare robots which assist humans. This evolutionary computation is realized by applying chaotic retrieval and soft DNA (soft computing oriented data driven functional scheduling architecture) on associative memories. We apply this evolutionary computation to multi-agent robots which move abreast and intelligent transport systems. Essentially, the process of this evolutionary computation is parallel processing. Therefore, we implement its parallel processing algorithm on A-NET (actors network) parallel object-oriented computer, and show the usefulness of parallel processing for the proposed evolutionary computation.
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paper_title: Fuzzy-associative-memory-based knowledge construction with an application to a human-machine interface

paper_content:

A fuzzy knowledge construction method is proposed for application to human-machine interfaces. This paper considers a human movement estimation system to be one of the interfaces. This estimation system transforms human physical movements into qualitative linguistic labels. For example, the degrees of magnitude and speed of the physical movement are represented by qualitative words. It is difficult to construct the transformation knowledge because the relation between the movements and the labels is fuzzy. This paper proposes a method of constructing the knowledge. This proposed method uses a fuzzy associative memory organizing units system and is applied to estimating human sports movements. Experimental results show that the proposed method is suitable for application to human-machine interfaces.
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paper_title: Using humanoid robots to study human behavior

paper_content:

Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans "program" behavior in-or train-each other.
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paper_title: Evolving pet robot with emotional model

paper_content:

Deals with a pet robot with an emotional model. The robot requires several capabilities, such as perceiving, acting, communicating and surviving. Furthermore, it should learn various behaviors through interaction with its owner. This paper focuses on teaching a pet robot tricks or to dance. Basically, the owner can teach these tricks by simple communication based on trial and error. The robot performs the tricks by using a fuzzy controller, and further acquires tricks by a delta rule for online learning and a genetic algorithm for off-line learning. We use "Rag Warrior" as our pet robot. Experimental results show that this robot performs tricks through interaction with its owner.
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paper_title: Failure diagnosis by trajectory parallel measure on attractor

paper_content:

The study of chaos is attracting attention, and a wide range of academic fields is actively involved. On the other hand, Aihara (1992) proposed the term "chaos engineering" to describe the application of chaos theory for engineering purposes, and its possibilities have been demonstrated. The approaches to the engineering applications of chaos are generally classified, into two groups: (1) creative methods and (2) analytical methods. In the former, chaos conditions, consistent with the particular application, are produced in any case, and this behavior is used in a positive manner. In the latter, a phenomenon is examined to see whether it is chaotic or not, and if it is a deterministic chaos, the latent rules involved are extracted and used for a practical purpose. Applications based on the creative methods include chaos computing, chaotic memory, chaos cipher communications, chaos art, applications using chaos fluctuations, chaos circuits, non-linear engineering systems, etc. applications using the analytical methods include failure diagnosis, deterministic non-linear short-term forecasting, identifying chaos, modeling bio-chaos, etc. This paper first describes approaches of chaotic diagnosis. Next, the trajectory parallel measure method for diagnosis is described. Finally, fault diagnosis is discussed as a prospective industrial application using practical examples.
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paper_title: Current-Sensor-Based Feed Cutting Force Intelligent Estimation and Tool Wear Condition Monitoring

paper_content:

Tool wear condition monitoring has the potential to play a critical role in ensuring the dimensional accuracy of the workpiece and prevention of damage to cutting equipment. It could also help in automating cutting processes. In this paper, the feed cutting force estimated with the aid of an inexpensive current sensor installed on the AC servomotor of a computerized numerical control tuning center is used to monitor tool wear condition. To achieve this, the feed drive system is modeled, using neuro-fuzzy techniques, to provide the framework for estimating the feed cutting force based on the feed motor current measured. Functional dependence of the feed cutting force on tool wear and cutting parameters are then expressed in the form of a difference equation relating variation in the feed cutting force to tool wear rate. The computerized system automatically compares successive feed cutting force estimates and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that the tool wear condition monitoring is effective and industrially applicable.
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paper_title: Wavelet packet feature extraction for vibration monitoring

paper_content:

Condition monitoring of dynamic systems based on vibration signatures has generally relied upon Fourier based analysis as a means of translating vibration signals in time domain into the frequency domain. However, Fourier analysis provided a poor representation of signals well localized in time. In this case, it is difficult to detect and identify the signal pattern from their expansion coefficients because the information is diluted across the whole basis. The wavelet packet transform is introduced as an alternative means of extracting time-frequency information from vibration signature. Moreover, with the aid of statistical based feature selection criteria, a lot of feature components containing little discriminant information could be discarded resulting in a feature subset with reduced number of parameters. This significantly reduces the long training time that is often associated with neural network classifier and increases the generalization ability of the neural network classifier.
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paper_title: Neural network compensation of gear backlash hysteresis in position-controlled mechanisms

paper_content:

This paper demonstrates that artificial neural networks can be used to identify and compensate for hysteresis caused by gear backlash in precision position-controlled mechanisms. A major contribution of this research is that physical analysis of the system nonlinearities and optimal control are used to design the neural network structure. Network sizing and initializing problems are thus eliminated. This physically meaningful, modular approach facilitates the integration of this neural network with existing controllers; thus, initial performance matches that of existing control approaches and then is improved by refining the parameter estimates via further learning. The neural network operates by recognizing backlash and switching to a control which moves smoothly through the backlash when the torque transmitted to the output shaft must be reversed.
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paper_title: Fuzzy rules generation using new evolutionary algorithms combined with multilayer perceptrons

paper_content:

Based on evolutionary algorithms (EAs) and multilayer perceptrons (MLPs), a fuzzy rules generation method inclusive of two main learning stages is presented in this paper. In the primary stage, a new EA is developed to generate numerical control rules from input-output data without the help of experts, which increases the diversity of individuals to reduce the opportunities of falling into local optima. Every generated numerical rule is accumulated in a lookup table called a numerical-rule-based controller (NRC). In the secondary stage, both antecedent and consequent variables of the numerical rules are fuzzified by training MLPs with the backpropagation algorithm. All training data are directly derived from the NRC with simple manipulations. Consequently, a linguistic-rule-based controller (LRC) consisting of the generated fuzzy rules is completed. Two illustrative experiments are successfully made on the computer simulation and hardware implementation of the NRCs and LRCs of different types using the new EA combined with the MLPs. The experimental results reveal that the proposed EA-MLP MLP approach is efficient and effective to generate fuzzy rules which control nonlinearly dynamical systems exceedingly well.
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paper_title: Design, fabrication, and real-time neural network control of a three-degrees-of-freedom nanopositioner

paper_content:

A nanometric precision three-degrees-of-freedom positioner is designed and fabricated. Actuation is based on piezoelectric stacks. Capacitive gap sensors with less than 1.0-nm resolution are used for position feedback. In order to design a proper closed-loop controller, the open-loop characteristics of the nanopositioner (static stiffness, hysteresis, drift, frequency response, and the coupling effects) are experimentally investigated. A cerebellar model articulation controller neural network control algorithm was applied in order to provide real-time learning and better tracking capability compared to a standard proportional-integral-derivative control algorithm.
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paper_title: Weld modeling and control using artificial neural networks

paper_content:

Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding (VPPAW) process. Three areas of welding application were investigated: weld process modeling, weld process control, and weld bead profile analysis for quality control. Experiments and analysis confirm that artificial neural networks are powerful tools for analysis, modeling, and control applications. They are particularly attractive in view of their capabilities to process nonlinear and noisy data, learn from actual welding data, and execute at relatively high speed. It is shown that neural networks are capable of modeling parameters of the VPPAW process to on the order of 10% accuracy or better. The same was observed when neural networks were used to select welding equipment parameters and the resulting bead geometries were estimated. These performance figures suggest that a VPPA welding control system can be implemented based on neural network models and control mechanisms.<<ETX>>
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paper_title: Use of fuzzy logic for modeling, estimation, and prediction in switched reluctance motor drives

paper_content:

Switched reluctance motor drives may be used in many commercial applications due to their simplicity and low cost. These drives require rotor position feedback to operate. However, in many systems, rotor position sensors have disadvantages. In this paper, a position sensorless scheme is described which uses fuzzy modeling, estimation and prediction. An important feature is that saturation and real-time nonideal effects are not ignored, but that no mathematical model is required. Instead, a fuzzy logic-based model is constructed from both static and real-time motor data, and from this model the rotor position is estimated. The system also incorporates fuzzy logic-based methods to provide a high robustness against noise. This includes a fuzzy predictive filter which combines both fuzzy logic-based time-series prediction, as well as a heuristic knowledge-based algorithm to detect and discard feedback signal error. In addition, the method uses heuristic knowledge to choose the most desirable phase for angle estimation in order to minimize the effect of feedback error. It is also shown that, by using fuzzy logic, the estimation scheme offers a high robustness and reliability and is thus well suited to a wide range of systems.
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paper_title: Decoupled stator-flux-oriented induction motor drive with fuzzy neural network uncertainty observer

paper_content:

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results.
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paper_title: Speed control of induction motor without rotational transducers

paper_content:

This paper describes a newly developed speed sensorless induction motor drive control scheme based on neural network techniques. The backpropagation neural network technique is used to provide a real-time adaptive identification of the motor speed. The estimation objective is defined in terms of a desired or target trajectory that the neural networks model output should match or trade as closely as possible. The backpropagation algorithm is used to adjust the motor speed so that the neural model output follows the target trajectory. This backpropagation mechanism forces the estimated speed to follow precisely the actual motor speed. The zero speed crossing phenomena is also described and experimental results are presented and analysed.
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paper_title: A simple direct-torque neuro-fuzzy control of PWM-inverter-fed induction motor drive

paper_content:

In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor TMS 320C31-based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterized by very fast torque and flux response, very-low-speed operation, and simple tuning capability.
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paper_title: Fuzzy-tuning current-vector control of a three-phase PWM inverter for high-performance AC drives

paper_content:

This paper proposes a new discrete fuzzy-tuning current-vector control (FTC) scheme for three-phase pulsewidth modulation (PWM) inverters. The proposed current control scheme can achieve fast transient responses and, at the same time, have very low total harmonic distortion in output current during steady-state operation. The proposed FTC scheme generates quasi-optimum PWM patterns by using a closed-loop control technique with instantaneous current feedback. The proposed FTC scheme has been realized using a single-chip digital signal processor (TMS320C14) from Texas Instruments. Experimental results are given to verify the proposed fuzzy-tuning current control strategy for three-phase PWM inverters.
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paper_title: Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

paper_content:

Fault diagnosis requires a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is needed which can be learned from experimental or simulated data. A fuzzy logic based diagnosis is advantageous. It allows an easy incorporation of a-priori known rules and also enables the user to understand the inference of the system. In this contribution, a new diagnosis scheme is presented and applied to a DC motor. The approach is based on a combination of structural a-priori knowledge and measured data in order to create a hierarchical diagnosis system that can be adapted to different motors. Advantages of the system are its high degree of transparency and an increased robustness.
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paper_title: Predictive control of a bench-scale chemical reactor based on neural-network models

paper_content:

The authors have developed a reliable long-range predictor, comprising two neural networks with external feedback in series, and investigated its applicability for model predictive control on a simulation example. The networks use external feedback of the process state, yielding a state-space mapping that eliminates the drawbacks of the input-output mapping of the feedforward networks. This paper applies the long-range predictor to the model predictive control of an experimental bench-scale semi-batch chemical reactor. Examples of yield maximization for a reaction with complex kinetics are used to assess the proposed predictive control scheme. Control performance is compared for predictors based on the proposed external-feedback networks and on conventional feedforward networks. Results for various operating conditions, disturbances, and included analytical models demonstrate the superiority of the proposed control scheme in experiments.
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paper_title: Applications of radial basis network technology to process control

paper_content:

Modern industrial process automation systems are organized as hierarchical systems, with at least three levels: field, direct control, and supervisory level. The paper presents applications of radial basis function (RBF) networks in each one of these levels. The first example illustrates the application of these networks to improve the performance of a control valve. The second example demonstrates the potential of an RBF network to deal with nonlinear processes as direct controller. The third example, illustrates the use of a supervisory system to tune proportional integral controllers, implemented in a distributed environment. These examples do not only present some applications of RBF technologies to process control, but also demonstrate, through practical examples, that this technology can be implemented in existing control systems.
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paper_title: Fuzzy modeling and expert optimization control for industrial processes

paper_content:

This paper presents the development of a real-time fuzzy expert optimization control system for industrial processes. The focus of this study is the application of fuzzy optimization control to a fluidized catalytic cracking unit (FCCU) in an oil refinery to optimize the cracking product distribution under a variable production environment. The system has been successfully implemented in a large production scale FCCU and the application results show significant benefits through fuzzy optimization control.
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paper_title: Data strip mining for the virtual design of pharmaceuticals with neural networks

paper_content:

A novel neural network based technique, called "data strip mining" extracts predictive models from data sets which have a large number of potential inputs and comparatively few data points. This methodology uses neural network sensitivity analysis to determine which predictors are most significant in the problem. Neural network sensitivity analysis holds all but one input to a trained neural network constant while varying each input over its entire range to determine its effect on the output. Elimination of variables through neural network sensitivity analysis and predicting performance through model cross-validation allows the analyst to reduce the number of inputs and improve the model's predictive ability at the same time. This paper demonstrates its effectiveness on a pair of problems from combinatorial chemistry with over 400 potential inputs each. For these data sets, model selection by neural sensitivity analysis outperformed other variable selection methods including the forward selection and genetic algorithm.
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paper_title: A neural-network approach to fault detection and diagnosis in industrial processes

paper_content:

Using a multilayered feedforward neural-network approach, the detection and diagnosis of faults in industrial processes that requires observing multiple data simultaneously are studied in this paper. The main feature of our approach is that the detection of the faults occurs during transient periods of operation of the process. A two-stage neural network is proposed as the basic structure of the detection system. The first stage of the network detects the dynamic trend of each measurement, and the second stage of the network detects and diagnoses the faults. The potential of this approach is demonstrated in simulation using a model of a continuously well-stirred tank reactor. The neural-network-based method successfully detects and diagnoses pretrained faults during transient periods and can also generalize properly. Finally, a comparison with a model-based method is presented.
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paper_title: Neural network model for paper forming process

paper_content:

Paper is made by a continuous high-speed filtration drainage of an aqueous suspension of fibers. This paper presents a new approach to the controllable simulation of paper forming, using artificial neural network methods. The model incorporates dynamics of the forming process, like turbulence, drainage speed, and preferential drainage through earlier less-dense regions and fiber properties, like propensity to clump, or "flocculate," fiber flexibility, and concentration of fibers in the suspension. Results for monofiber layer structures are described, showing effects of turbulence and its decay during drainage in causing clumping, or "flocculation." The commercial process has, as one of its main goals, the reduction to tolerable levels of the nonuniformity in mass distribution resulting from flocculation. The new model yields data corresponding to that obtainable along arbitrary scanning lines in planar stochastic fibrous structures, providing profiles, variances, histograms of local areal density, and histograms of local free-fiber lengths. These results closely resemble experimental data from commercial paper samples obtained from radiographic or optical transmission images subjected to image analysis.
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paper_title: Target value computing in paper machine grade changes by fuzzy system

paper_content:

A fuzzy system is proposed to compute new target values for low level controllers during grade changes in a paper machine. The fuzzy system is designed and tuned in co-operation with human operators of the paper machine. The design method utilizes both heuristic knowledge and estimation techniques based on input-output data. The input fuzzy sets and the rule base are initially designed verbally and thereafter fine-tuned by a least squares algorithm. An industrial example illustrates the iterative design process. It shows that transparency and a priori knowledge included in the model are preserved also during optimization of the fuzzy model.
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paper_title: A novel real-time fuzzy-based diagnostic system of roll eccentricity influence in finishing hot strip mills

paper_content:

This paper proposes a method for the diagnosis of roll eccentricity influence on the strip thickness at the exit of a finishing hot strip mill (FHSM). Each exit strip thickness defect is related to one roll of the FHSM, allowing the implementation of an optimal policy for the substitution and maintenance of the rolls, while maintaining the required quality level of the strip. This policy allows the minimization of roll changes and the concentration of several changes at the same time, reducing production costs. Fuzzy logic is used to compare spectra, looking for common patterns, which enables a totally-automated diagnostic system. Likewise, as part of the solution, a novel estimate of roll eccentricity, based on a least-squares algorithm has been developed, which provides higher accuracy than classical algorithms as well as a drastic reduction in the time required to perform eccentricity tests.
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paper_title: Application of fuzzy logic control for continuous casting mold level control

paper_content:

This paper deals with the problem of molten metal level control in continuous casting. Under normal circumstances, proportional integral derivative (PID) control performs quite well, but abnormal conditions (in particular nozzle clogging/unclogging) require manual intervention. Indeed, when the flow of matter into the mold increases suddenly, the PID controller is not always able to prevent large level variations that can even lead to mold overflow. So, a fuzzy controller has been designed using the expert knowledge of the operators for controlling the process during disturbed phases. The paper discusses both the design of the fuzzy logic controller and its integration with the PID in a global control architecture. Results from simulation and successful online implementation are presented.
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paper_title: Distributed diagnosis system combining the immune network and learning vector quantization

paper_content:

A distributed diagnosis system combining the immune network (IN) and learning vector quantization (LVQ) is proposed for accurately detecting faulty sensor outputs in control plants. The system has two execution modes, namely, its training mode, where the LVQ extracts a correlation between each two sensors from their outputs when they work properly, and its diagnosis mode, where the LVQ contributes to testing each two sensors using the extracted correlation, and the IN contributes to determining faulty sensors by integrating the local testing results obtained from the LVQ. With the proposed method, faulty sensors, such as age deteriorated ones, which have been difficult to be detected only by checking each sensor output independently, can be specified.
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paper_title: Neural intelligent control for a steel plant

paper_content:

The improvement of the performances of a complex production process such as the Sollac hot dip galvanizing line of Florange (France) needs to integrate various approaches, including quality monitoring, diagnosis, control, optimization methods, etc. These techniques can be grouped under the term of intelligent control and aim to enhance the operating of the process as well as the quality of delivered products. The first section briefly describes the plant concerned and presents the objectives of the study. These objectives are mainly reached by incorporating the skill of the operators in neural models, at different levels of control. The low-level supervision of measurements and operating conditions are briefly presented. The control of the coating process, highly nonlinear, is divided in two parts. The optimal thermal cycle of alloying is determined using a radial basis function neural network, from a static database built up from recorded measurements. The learning of the weights is carried out from the results of a fuzzy C-means clustering algorithm. The control of the annealing furnace, the most important equipment, is achieved by mixing a static inverse model of the furnace based on a feedforward multilayer perceptron and a regulation loop. Robust learning criteria are used to tackle possible outliers in the database. The neural network is then pruned in order to enhance the generalization capabilities.
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paper_title: Design and implementation of a fuzzy elevator group control system

paper_content:

Elevator group control systems (EGCSs) are the control systems that systematically manage three or more elevators in order to efficiently transport passengers. Most EGCSs have used the hall call assignment method to assign elevators in response to passengers' calls. This paper proposes a control strategy generation method, a hall call assignment method based on the fuzzy theory, and then the fuzzy elevator group control system (FEGCS). The control strategy of FEGCS is made using the classification of the passenger traffic and system manager's requirements, and the hall calls are assigned to suitable elevators by the generated control strategy. The system is operated using the given control strategy which is defined by the system manager. The proposed system shows better results than the conventional methods in simulations.
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paper_title: An elevator group control system with floor-attribute control method and system optimization using genetic algorithms

paper_content:

A new concept is proposed for an elevator group control system which can change control settings according to individual floor utilization situations. The floor-attribute-based control method uses a combination of floor-attribute-based evaluation and car-attribute-based evaluation. Computer simulations show it can improve multiple preferential door settings at the same time. Additionally, an on-line parameter tuning method using genetic algorithms is proposed. The floor attribute control method needs several dozen control parameters to be tuned according to each building's traffic, and the proposed method can do it. The elitist-preserving strategy is an effective way to keep control stability. A diversity-recovering operation, which initializes population, excluding the elitist individual, when traffic flow data have changed, improves the following ability for transitions of utilized conditions.
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paper_title: GA-based fuzzy PI/PD controller for automotive active suspension system

paper_content:

A genetic-algorithm (GA)-based fuzzy proportional-plus-integral-proportional-plus-derivative (PI/PD) controller is proposed for an automotive active suspension system (AASS). The fuzzy PI- and PD-type controllers are combined to cope with the different road conditions. By using the merit of GAs, the optimal decision-making rules for both types of controllers are constructed. The real-time simulation results demonstrate that the fusion of GAs and fuzzy controller for an AASS can provide passengers much more ride comfort.
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paper_title: Neuro fuzzy transmission control for automobile with variable loads

paper_content:

A transmission controller for an automobile with variable loads is designed and tested by using a transmission system (Isuzu NAVi-5) consisting of actuators and sensors besides gears, a throttle, and a clutch. This system engages and disengages the clutch and gears up and down automatically. In other words, a manual gear-shift system is automated. As neuro and fuzzy approaches are promising methods to interface between a vehicle operator and an automobile, they are used to design a transmission controller. Automobile loads are estimated from the signals of status sensors by fuzzy logic. Vehicle driver's intention is inferred by fuzzy logic using the information from automobile status sensors on which driver's intention is reflected. These are then fed into a neural network. An experienced driver (much data) teachers the neural network an optimal gear-shift scheduling such that a vehicle operator feels comfortable even when automobile loads change (for example when an automobile is traveling up and down hill). >
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paper_title: Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches

paper_content:

The objective of this work is to model driver behaviour in the area of route selection. The research focuses on an optimum route search function in a typical in-car navigation system or dynamic route guidance system. In this work, the authors emphasize the need to orientate the route selection method on the driver's preference. Each route candidate has a set of attributes. A fuzzy-neural approach is used to represent the correlation of the attributes with the driver's route selection. A recommendation or route ranking can be provided to the driver. Based on a training of the fuzzy-neural net on the driver's choice, the route selection function can be made adaptive to the decision-making of the driver.
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paper_title: Improvement of speed control performance using PID type neurocontroller in an electric vehicle system

paper_content:

In order to develop an efficient driving system for electric vehicle (EV), a testing system using motors has been built to simulate the driving performance of EVs. In the testing system, the PID controller is used to control rotating speed of motor when the EV drives. In this paper, in order to improve the performance of speed control, a neural network is applied to tuning parameters of PI controller. It is shown,through experiments that a neural network can reduce output error effectively while the PI controller parameters are being tuned online. >
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paper_title: Fuzzy logic two-phase traffic signal control for coordinated one-way streets

paper_content:

The specific goal of this study was to compare the differences in various traffic signal control algorithms when simulating two consequent one-way intersections with no turning traffic. The opportunities offered by fuzzy logic for controlling traffic are the subject of our FUSICO (FUzzy logic SIgnal group COntrol) research project. The compared algorithms used in the simulations were coordinated fixed-time signal group control, traffic-actuated gap-seeking non-coordinated signal group control, traffic-actuated FUSICO, and a combination of standard traffic-actuated control and a FUSICO controller. This study was our first attempt to use fuzzy methods in coordinated traffic signals; the results were promising. All previous results of the FUSICO project have indicated better overall efficiency than traditional vehicle-actuated control.
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paper_title: Vehicle dynamics simulation based on hybrid modeling

paper_content:

Regarding the mechanical engineering area, over the last 40 years a lot of effort has been undertaken to find very exact descriptions for the dynamic behavior of road vehicles based on mathematical models. All those models include certain parameter values which may be taken from data sheets or which have to be measured or determined by real driving tests. Using these physical models for vehicle simulation purposes, the problem arises, that some of the model parameters are time-variant. They vary over a smaller or larger time period, e.g. due to aging, different vehicle loads or changing environmental conditions like a transition from dry to wet or icy road. Parameter variations lead to systematic modeling errors which makes simulation results turn out incorrect. To overcome that problem, this paper describes the use of hybrid models to reduce modeling errors. Within hybrid models, conventional mathematical process models are combined with adaptive learning structures, e.g. neural networks. In this contribution, an extended radial basis function network called LOLIMOT (local linear model tree) is used to compensate the influences of changing road conditions affecting a vehicle dynamics simulation model.
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paper_title: Optimising train movements through coast control using genetic algorithms

paper_content:

A genetic algorithm (GA) is proposed to optimise train movements using appropriate coast control that can be integrated within automatic train operation (ATO) systems. The coast control output for a train changes with the interstation distances and gradient profiles, and the current operating conditions of the mass rapid transit (MRT) system, namely: (i) train schedules; (ii) expected passenger loads; and (iii) expected track voltages. The algorithm generates an optimum coast control based on evaluation of the punctuality, riding comfort and energy consumption. Before the train sets off to the designated station, a coast control table is generated that will be referenced by the train at runtime for deciding when to initiate coasting or resume motoring control. Each coast control table is encoded into variable length chromosomes with each gene representing the relative position between stations where coasting should be initiated or terminated. Each generation is evolved from mating of the paired equal-length chromosomes with possibilities of crossover, mutations, gene duplications and gene deletions. The key feature of this method is that it has a solid mathematical foundation. Effectively, the implementation provides good, credible and reasonably fast solutions for this variable dimensional and multiobjective optimisation problem. The algorithm has the potential for online implementation for producing a coast control lookup table for each interstation run before the train sets off. The results, although preliminary, suggest that the method is promising.
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paper_title: Fuzzy neural networks for machine maintenance in mass transit railway system

paper_content:

This paper describes an application of fuzzy knowledge-based neural-network system (FKNNS) being developed by the Hong Kong Mass Transit Railway Corporation (MTRC) for the maintenance of its ticket machines in one of the busiest transit systems in the world. The model utilizes specific experts' knowledge which is transformed into fuzzy membership functions through certain control rules. The error backpropagation network was selected for the network training in which various activation functions were tested. After extensive training of the network, the FastProp with hyperbolic tangent was recommended, Input patterns were decomposed to facilitate the training process and eliminate the effect of local minima. Both the test and forecast results indicated that the FKNNS is an excellent aid for machine maintenance planning since there are too much difficulties in deriving the analytical solution otherwise. Beta test result shows a 20.08% improvement over the existing maintenance methodology. Moreover, the developed model can smoothly handle more types of industrial machine maintenance problems and generate intangible benefits toward MTRC in terms of improved customer service and better corporation image.
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paper_title: From Computing With Numbers To Computing With Words From Manipulation Of Measurements To Manipulation of Perceptions

paper_content:

Discusses a methodology for reasoning and computing with perceptions rather than measurements. An outline of such a methodology-referred to as a computational theory of perceptions is presented in this paper. The computational theory of perceptions, or CTP for short, is based on the methodology of CW. In CTP, words play the role of labels of perceptions and, more generally, perceptions are expressed as propositions in a natural language. CW-based techniques are employed to translate propositions expressed in a natural language into what is called the Generalized Constraint Language (GCL). In this language, the meaning of a proposition is expressed as a generalized constraint, N is R, where N is the constrained variable, R is the constraining relation and isr is a variable copula in which r is a variable whose value defines the way in which R constrains S. Among the basic types of constraints are: possibilistic, veristic, probabilistic, random set, Pawlak set, fuzzy graph and usuality. The wide variety of constraints in GCL makes GCL a much more expressive language than the language of predicate logic. In CW, the initial and terminal data sets, IDS and TDS, are assumed to consist of propositions expressed in a natural language. These propositions are translated, respectively, into antecedent and consequent constraints. Consequent constraints are derived from antecedent constraints through the use of rules of constraint propagation. The principal constraint propagation rule is the generalized extension principle. The derived constraints are retranslated into a natural language, yielding the terminal data set (TDS). The rules of constraint propagation in CW coincide with the rules of inference in fuzzy logic. A basic problem in CW is that of explicitation of N, R, and r in a generalized constraint, X is R, which represents the meaning of a proposition, p, in a natural language.
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Title: Industrial Applications of Soft Computing: A Review

Section 1: INTRODUCTION
Description 1: Discuss the historical development and importance of soft computing in industrial applications.

Section 2: INDUSTRIAL INNOVATION USING SOFT COMPUTING
Description 2: Explore the core concepts, benefits, and advancements of soft computing in industrial sectors.

Section 3: INDUSTRIAL APPLICATION FIELDS
Description 3: Examine various industrial fields where soft computing has been significantly applied, including specific applications and methods.

Section 4: APPLICATION FIELDS
Description 4: Investigate different specific fields such as consumer appliances, cooling and heating, washing, food preparation, and other areas where soft computing has been applied.

Section 5: CONTROL AND MONITORING
Description 5: Detail the role of soft computing in control and monitoring systems across industries.

Section 6: OPERATIONS
Description 6: Discuss the optimization and control of industrial operations through soft computing techniques.

Section 7: PLANNING
Description 7: Review the applications of soft computing in planning and strategizing industrial processes.

Section 8: MANUFACTURING AUTOMATION AND ROBOTICS
Description 8: Evaluate the use of soft computing in manufacturing technologies and robotic systems.

Section 9: MOTION CONTROL (INCLUDING WELDING)
Description 9: Analyze the specific applications of soft computing in motion control and welding processes.

Section 10: POWER ELECTRONICS
Description 10: Discuss the impact of soft computing on power electronics and related control systems.

Section 11: PROCESS ENGINEERING
Description 11: Explore applications in chemical, paper, and steel process industries facilitated by soft computing.

Section 12: TRANSPORTATION
Description 12: Review the implementation of soft computing in various transportation-related applications including road, rail, and building transportation.

Section 13: FUTURE OPPORTUNITIES
Description 13: Outline future trends and opportunities for soft computing in industrial applications.

Section 14: CONCLUSION
Description 14: Summarize the findings and conclude the potential of soft computing in bringing innovative solutions to industry.