A review of neuro-fuzzy systems based on intelligent control
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paper_title: Neural-network-based fuzzy logic control and decision system

paper_content:

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >
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paper_title: Neural-network-based fuzzy logic control and decision system

paper_content:

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >
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Title: A Review of Neuro-Fuzzy Systems Based on Intelligent Control
Section 1: Introduction
Description 1: Introduce the importance of fuzzy logic and its applications, and explain the concept of neuro-fuzzy systems.

Section 2: Intelligent Control
Description 2: Discuss the definition and attributes of intelligent control including correctness, robustness, extensibility, and reusability & compatibility.

Section 3: Neural Network
Description 3: Explain the structure, function, and importance of neural networks in control systems.

Section 4: Defining some Fuzzy Operations
Description 4: Define basic fuzzy operations like union, intersection, and complement of fuzzy sets.

Section 5: Architecture of Fuzzy Logic Controllers
Description 5: Describe the basic configuration of fuzzy logic controllers, including its parts and their functions.

Section 6: Neuro-Fuzzy Structure
Description 6: Explain the neuro-fuzzy cooperative model and present an example to illustrate its application.

Section 7: Fuzzy Control of an Unmanned Vehicle
Description 7: Discuss a specific application of neuro-fuzzy control in unmanned vehicles, highlighting the learned membership functions and fuzzy logic rules.