Utilizing neural networks in magnetic media modeling and field computation: A review
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paper_title: Elements Of Artificial Neural Networks

paper_content:

History of neural networks supervised learning - single-layer networks supervised learning - multilayer networks I supervised learning - multilayer networks II unsupervised learning associative models optimization methods a little math data.
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paper_title: Using neural networks in the identification of Preisach-type magnetostriction and field-temperature models

paper_content:

It is well known that the identification process of Preisach models is, basically, reduced to the determination of weight functions of the operators upon which the model is built. Recently, two-dimensional Preisach models have been proposed as a possible approach for modeling one-dimensional magnetostriction as well as field-temperature effects on magnetized bodies. In this article, the use of neural network machinery is suggested to solve identification problems of such models. The suggested technique has been implemented and experimentally tested. Testing results suggests that this technique can lead to reasonably accurate simulation results.
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paper_title: Identification of vector Preisach models from arbitrary measured data using neural networks

paper_content:

It has been recently shown that the identification process of scalar, as well as some family of vector, Preisach-type models may be accomplished by the aid of artificial neural networks. Our purpose in this article is to further generalize the approach through which neural networks may be utilized for the identification of vector Preisach models, while using arbitrary measured data for a magnetic recording tape sample. Using the proposed approach, the identification process has been performed and additional simulations have been carried out and compared to corresponding measurements. Comparison results suggest that the proposed technique can lead to good agreement between measured and predicted values.
---
paper_title: Using neural networks in the identification of Preisach-type hysteresis models

paper_content:

The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.
---
paper_title: Elements Of Artificial Neural Networks

paper_content:

History of neural networks supervised learning - single-layer networks supervised learning - multilayer networks I supervised learning - multilayer networks II unsupervised learning associative models optimization methods a little math data.
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paper_title: Automated two-dimensional field computation in nonlinear magnetic media using Hopfield neural networks

paper_content:

It is well known that the computation of magnetic fields in nonlinear magnetic media may be carried out using different approaches. In the case of problems involving complex geometries and/or magnetic media, numerical techniques become especially more appealing. In this paper, we present an automated integral equation approach using which two-dimensional field computations may be carried out in nonlinear magnetic media. This approach is constructed in terms of a continuous Hopfield neural network (HNN) whose neuron activation functions are set to mimic the vectorial magnetic properties of the media. Using well-established HNN energy minimization algorithms, an automated solution of the problem is then obtained. The approach has been implemented and resulted in good agreement with finite-element (FE) computations. Details of the approach, computations, and FE results are given in this paper.
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paper_title: Utilizing hopfield neural networks in the analysis of reluctance motors

paper_content:

Reluctance motors are currently being used widely in different applications. Sometimes, the rotor inherent saliency may introduce some difficulty in pursuing an analytical solution to the motor electromagnetic field problem. In this paper, Hopfield artificial neural networks are used to minimize the air-gap magnetic energy function. Thus, a numerical electromagnetic field solution is obtained automatically. Performance of the motor may then be computed from the obtained field solution. Simulations for a motor having typical dimensions are presented in the paper. It is found that the results of these simulations are in full agreement with reported results as well as well known theoretical aspects.
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paper_title: Elements Of Artificial Neural Networks

paper_content:

History of neural networks supervised learning - single-layer networks supervised learning - multilayer networks I supervised learning - multilayer networks II unsupervised learning associative models optimization methods a little math data.
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paper_title: Efficient vector hysteresis modeling using rotationally coupled step functions

paper_content:

Abstract Vector hysteresis models are usually used as sub-modules of field computation software tools. When dealing with a massive field computation problem, computational efficiency and practicality of such models become crucial. In this paper, generalization of a recently proposed computationally efficient vector hysteresis model based upon interacting step functions is presented. More specifically, the model is generalized to cover vector hysteresis modeling of both isotropic and anisotropic magnetic media. Model configuration details as well as experimental testing and simulation results are given in the paper.
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paper_title: Vector hysteresis modeling using octal clusters of coupled step functions

paper_content:

Recently, substantial efforts have been focused on the efficiency enhancement of vector Preisach-type model implementation methodologies. This paper presents a computationally efficient approach for implementing a vector Preisach-type hysteresis model through an ensemble of octal clusters of coupled step functions. Within this approach, each cluster can be tuned by proper selection of coupling factors to exhibit vector hysteresis properties, thus minimizing number of clusters needed to construct a vector Preisach-type model.
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paper_title: Identification and testing of an efficient hopfield neural network magnetostriction model

paper_content:

Abstract Magnetostriction models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. This paper presents an efficient magnetostriction Preisach-type model based on the effective field approach. According to this approach, the total applied field may be regarded as a super position of the actual magnetic field and a stress-dependent feedback term. Construction and identification of the model is carried out by providing experimental training data to a modular discrete Hopfield neural network–linear neural network combination. Experimental testing suggests that this model can lead to good qualitative and quantitative simulation results.
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paper_title: Implementation of magnetostriction Preisach-type models using orthogonally coupled hysteresis operators

paper_content:

Magnetic materials exhibiting gigantic magnetostriction are currently being used in various actuator devices and vibration damping applications. Recently, a new family of efficient Preisach-type vector hysteresis models having coupled elementary operators has been introduced. The purpose of this paper is to extend the applicability of those recently introduced models to magnetostriction simulation. Details of the model, its identification, and experimental testing are presented in the paper.
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paper_title: Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

paper_content:

Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper.
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paper_title: Identification and testing of an efficient hopfield neural network magnetostriction model

paper_content:

Abstract Magnetostriction models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. This paper presents an efficient magnetostriction Preisach-type model based on the effective field approach. According to this approach, the total applied field may be regarded as a super position of the actual magnetic field and a stress-dependent feedback term. Construction and identification of the model is carried out by providing experimental training data to a modular discrete Hopfield neural network–linear neural network combination. Experimental testing suggests that this model can lead to good qualitative and quantitative simulation results.
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paper_title: A mechanism of magnetic hysteresis in heterogeneous alloys

paper_content:

It is suggested that in many ferromagnetic materials there may occur particles distinct in magnetic character from the general matrix, and below the critical size, depending on shape, for which domain boundary formation is energetically possible. For such single-domain particles, change of magnetization can take place only by rotation of the magnetization vector. As the field changes continuously, the resolved magnetization may change discontinuously at critical values of the field. The character of the magnetization curves depends on the degree of magnetic anisotropy of the particle and on the orientation of easy axes with respect to the field. The magnetic anisotropy may arise from the shape of the particle, from magnetocrystalline effects, and from strain. A detailed quantitative treatment is given of the effect of shape anisotropy when the particles have the form of ellipsoids of revolution, along with a less detailed treatment for the general ellipsoidal form.
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paper_title: Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

paper_content:

Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper.
---
paper_title: Identification of vector Preisach models from arbitrary measured data using neural networks

paper_content:

It has been recently shown that the identification process of scalar, as well as some family of vector, Preisach-type models may be accomplished by the aid of artificial neural networks. Our purpose in this article is to further generalize the approach through which neural networks may be utilized for the identification of vector Preisach models, while using arbitrary measured data for a magnetic recording tape sample. Using the proposed approach, the identification process has been performed and additional simulations have been carried out and compared to corresponding measurements. Comparison results suggest that the proposed technique can lead to good agreement between measured and predicted values.
---
paper_title: Using neural networks in the identification of Preisach-type hysteresis models

paper_content:

The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.
---
paper_title: Identification and testing of an efficient hopfield neural network magnetostriction model

paper_content:

Abstract Magnetostriction models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. This paper presents an efficient magnetostriction Preisach-type model based on the effective field approach. According to this approach, the total applied field may be regarded as a super position of the actual magnetic field and a stress-dependent feedback term. Construction and identification of the model is carried out by providing experimental training data to a modular discrete Hopfield neural network–linear neural network combination. Experimental testing suggests that this model can lead to good qualitative and quantitative simulation results.
---
paper_title: Implementation of magnetostriction Preisach-type models using orthogonally coupled hysteresis operators

paper_content:

Magnetic materials exhibiting gigantic magnetostriction are currently being used in various actuator devices and vibration damping applications. Recently, a new family of efficient Preisach-type vector hysteresis models having coupled elementary operators has been introduced. The purpose of this paper is to extend the applicability of those recently introduced models to magnetostriction simulation. Details of the model, its identification, and experimental testing are presented in the paper.
---
paper_title: Automated two-dimensional field computation in nonlinear magnetic media using Hopfield neural networks

paper_content:

It is well known that the computation of magnetic fields in nonlinear magnetic media may be carried out using different approaches. In the case of problems involving complex geometries and/or magnetic media, numerical techniques become especially more appealing. In this paper, we present an automated integral equation approach using which two-dimensional field computations may be carried out in nonlinear magnetic media. This approach is constructed in terms of a continuous Hopfield neural network (HNN) whose neuron activation functions are set to mimic the vectorial magnetic properties of the media. Using well-established HNN energy minimization algorithms, an automated solution of the problem is then obtained. The approach has been implemented and resulted in good agreement with finite-element (FE) computations. Details of the approach, computations, and FE results are given in this paper.
---
paper_title: Identification and testing of an efficient hopfield neural network magnetostriction model

paper_content:

Abstract Magnetostriction models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. This paper presents an efficient magnetostriction Preisach-type model based on the effective field approach. According to this approach, the total applied field may be regarded as a super position of the actual magnetic field and a stress-dependent feedback term. Construction and identification of the model is carried out by providing experimental training data to a modular discrete Hopfield neural network–linear neural network combination. Experimental testing suggests that this model can lead to good qualitative and quantitative simulation results.
---
paper_title: Implementation of magnetostriction Preisach-type models using orthogonally coupled hysteresis operators

paper_content:

Magnetic materials exhibiting gigantic magnetostriction are currently being used in various actuator devices and vibration damping applications. Recently, a new family of efficient Preisach-type vector hysteresis models having coupled elementary operators has been introduced. The purpose of this paper is to extend the applicability of those recently introduced models to magnetostriction simulation. Details of the model, its identification, and experimental testing are presented in the paper.
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paper_title: Utilizing Anisotropic Preisach-type Models In The Accurate Simulation Of Magnetostriction

paper_content:

Magnetostriction models are being widely used in the development of fine positioning and active vibration damping devices. This paper presents a new approach for simulating 1-D magnetostriction using 2-D anisotropic Preisach-type models. In this approach, identification of the model takes into account measured flux density versus field and strain versus field curves for different stress values. Consequently, a more accurate magnetostriction model may be obtained. Details of the identification procedure as well as experimental testing of the proposed model are given.
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paper_title: Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

paper_content:

Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper.
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paper_title: Modeling Magnetic Hysteresis Under DC-Biased Magnetization Using the Neural Network

paper_content:

The excitation conditions of electrical steel are generally sinusoidal but, with the advent of power electronics in recent years, dc-biased excitation is sometimes experienced. The use of an iron core under dc-biased magnetization gives rise to asymmetrical hysteresis loops and the hysteresis loss in the iron core also increases with the value of dc excitation. For iron cores working with dc-biased excitation, accurate modeling of the nonlinear characteristics for the iron core that includes the dc-bias is very important for the computation of the exciting current and the iron loss. In this paper, an efficient approach for simulating the hysteresis loop of iron core under dc-biased excitation using neural-network theory is presented. The proposed method has the merits that a specific hysteresis loop can be identified conveniently and effectively to ensure that accurate electromagnetic-field analysis can be realized.
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paper_title: The use of neural networks in describing magnetisation phenomena

paper_content:

Abstract The application of multilayer feed-forward neural network architecture is presented for mapping of the B — H plane with a major loop and first order transition curves, and for approximation of dynamic hysteresis of soft magnetic specimens. The neural network correctly models hysteresis characteristics and constitutes a viable alternative to analytical approaches.
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paper_title: A model of anisotropic grain-oriented steel

paper_content:

In this work the magnetic flux distribution in the grain-oriented steel core transformer is investigated with the finite element method. A mathematical model (Model II) is obtained for the reluctivity tensor. The model, optimized by means of experimental values of the B-H curve, predicts different directions of the magnetization field, not just along the rolling and transverse directions of the material. The results are the flux maps, as well as the reactance values and the electrodynamic forces on the windings. These results, obtained by means of the isotropic model and the anisotropic first model [1], are compared with the experimental ones.
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paper_title: Using The Particle Swarm Evolutionary Approach in Shape Optimization and Field Analysis of Devices Involving Non-linear Magnetic Media

paper_content:

In the past years extensive efforts have been directed towards the development of shape optimization methodologies for electromagnetic devices. In cases when nonlinear magnetic behavior is expected to affect performance, the optimization process becomes more complicated and has to be closely coupled with field computation techniques capable of handling this non-linearity. Common examples involved the exchange of computational results between an optimization module and a finite element package to achieve the optimum shape design. Recently, the particle swarm optimization (PSO) evolutionary approach has been successfully utilized in the computation of magnetic fields for devices involving non-linear magnetic media. The purpose of this paper is to demonstrate how shape optimization and field analysis of such devices may be carried out using the same PSO computation module and without the involvement of other computational tools.
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paper_title: Neural-Network-Based Model for Dynamic Hysteresis in the Magnetostriction of Electrical Steel Under Sinusoidal Induction

paper_content:

A model is presented to calculate the dynamic hysteresis behaviour of the magnetostriction in electrical steel under 1-D sinusoidal magnetisation. The input of the model is one period of the 1-D induction wave. On this induction wave, a fast Fourier transform (FFT) is performed, of which the frequency components are fed to the input of a neural network (NN).This neural network calculates the frequency components (amplitude and phase) of the magnetostriction, on which an inverse fast Fourier transform (IFFT) is performed, thus obtaining the magnetostriction wave complementary to the input induction wave. The filtering technique was used to model the magnetostriction of a grain oriented electrical steel. The amplitude of the induction in the sample ranged between 0.6T and 1.8T and the frequency ranged between quasi-static and 200Hz.
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paper_title: Using neural networks in the identification of Preisach-type magnetostriction and field-temperature models

paper_content:

It is well known that the identification process of Preisach models is, basically, reduced to the determination of weight functions of the operators upon which the model is built. Recently, two-dimensional Preisach models have been proposed as a possible approach for modeling one-dimensional magnetostriction as well as field-temperature effects on magnetized bodies. In this article, the use of neural network machinery is suggested to solve identification problems of such models. The suggested technique has been implemented and experimentally tested. Testing results suggests that this technique can lead to reasonably accurate simulation results.
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paper_title: Generalized Preisach model of hysteresis

paper_content:

The model has the following advantages over the classical Preisach (1935) model. First, it relaxes the congruency property of the classical model, which results in a broader area of applicability. For example, the hysteresis loops exhibited by the Stoner-Wohlfarth (1948) magnetic particles can be described by the nonlinear model, while they are not covered by the classical model. Second, the nonlinear model allows one to fit first- and second-order transition curves, while the classical model allows only for fitting of first-order transition curves. As a consequence, the nonlinear model is more accurate. >
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paper_title: A Neural Network Modeling of Stress Behavior in Nonlinear Magnetostrictive Materials

paper_content:

Using accurate magnetostriction simulation models during the various design stages of their related devices can positively contribute to the enhancement of their precision. These models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. In this paper, we present a new contribution for the dynamic hysteresis behavior of magnetostrictive materials. To do this, we have used a neural network to model the relationship between the elongation λ and the magnetization M for different loads σ. The derivative of λ according to M is then calculated numerically and integrated in the Jiles–Atherton model for calculating the hysteresis loops.
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paper_title: The use of neural networks in magnetic hysteresis identification

paper_content:

Abstract The paper describes an approach to the problem of magnetic hysteresis identification based on the use of neural networks. The problem for many of the models currently being proposed for representing hysteresis in analytical systems is the identification of the model, i.e. the reliable determination of the appropriate parameters for a particular material. Some work has been published in the use of neural networks in this area and this paper seeks to extend this approach. In addition, the networks being considered are structured in such a manner that the information acquired by the network may be extracted at the end of the learning process - thus providing explicit values of the parameters, if needed. The basic methodologies being considered are based on the Preisach models and neural networks. Two techniques structured around the use of radial basis functions and a CMAC architecture are examined. The CMAC paradigm can allow incremental training and thus the retraining of the network for a new hysteresis curve can take place relatively quickly. Once the parameters have been derived, the model can be used within a conventional finite element analysis system.
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paper_title: Mathematical models of hysteresis and their applications

paper_content:

The classical Preisach model of hysteresis, Generalized scalar Preisach models of hysteresis, Vector Preisach models of hysteresis, Stochastic aspects of hysteresis, Superconducting hysteresis, Eddy current hysteresis. Core losses.
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paper_title: Efficient vector hysteresis modeling using rotationally coupled step functions

paper_content:

Abstract Vector hysteresis models are usually used as sub-modules of field computation software tools. When dealing with a massive field computation problem, computational efficiency and practicality of such models become crucial. In this paper, generalization of a recently proposed computationally efficient vector hysteresis model based upon interacting step functions is presented. More specifically, the model is generalized to cover vector hysteresis modeling of both isotropic and anisotropic magnetic media. Model configuration details as well as experimental testing and simulation results are given in the paper.
---
paper_title: Identification of vector Preisach models from arbitrary measured data using neural networks

paper_content:

It has been recently shown that the identification process of scalar, as well as some family of vector, Preisach-type models may be accomplished by the aid of artificial neural networks. Our purpose in this article is to further generalize the approach through which neural networks may be utilized for the identification of vector Preisach models, while using arbitrary measured data for a magnetic recording tape sample. Using the proposed approach, the identification process has been performed and additional simulations have been carried out and compared to corresponding measurements. Comparison results suggest that the proposed technique can lead to good agreement between measured and predicted values.
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paper_title: A hybrid model of Prandtl-Ishlinskii operator and neural network for hysteresis compensation in piezoelectric actuators

paper_content:

A hybrid model is proposed with the Prandtl-Ishlinskii operator and feedforward neural network. The hybrid model consists of three layers, where play operator is the transfer function of first layer. The training algorithm for model identification is derived from the backpropagation algorithm. With the measured data of piezoelectric actuator, a simulation is performed and shows that the hybrid model can precisely describe the asymmetric hysteresis loops. Incorporating the hybrid model into a feedforward controller, experiments indicate that the hybrid model exhibits a good performance for real-time control and the maximal tracking error is reduced by 76.75% with feedforawrd and PID controller.
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paper_title: Experimental testing of the average Preisach model of hysteresis

paper_content:

The authors report the results of extensive experimental testing of the average Preisach model of hysteresis. This testing has been performed for the following magnetic tape materials: Ampex-641, Ampex-797, Ampex-D1, TDK-VHS, Maxell-Beta, and Memorex-3480. Experimental results have revealed the consistent and remarkable accuracy of the average model. It is believed that this accuracy is related to the fact that the average model provides the far-reaching generalization of the congruency property of the classical Preisach model. Actually, this generalization goes much further than in the cases of the input-dependent or restricted Preisach models, although the average model needs the same experimental data for its identification as the two aforementioned models. >
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paper_title: Vector hysteresis modeling using octal clusters of coupled step functions

paper_content:

Recently, substantial efforts have been focused on the efficiency enhancement of vector Preisach-type model implementation methodologies. This paper presents a computationally efficient approach for implementing a vector Preisach-type hysteresis model through an ensemble of octal clusters of coupled step functions. Within this approach, each cluster can be tuned by proper selection of coupling factors to exhibit vector hysteresis properties, thus minimizing number of clusters needed to construct a vector Preisach-type model.
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paper_title: Identification and testing of an efficient hopfield neural network magnetostriction model

paper_content:

Abstract Magnetostriction models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. This paper presents an efficient magnetostriction Preisach-type model based on the effective field approach. According to this approach, the total applied field may be regarded as a super position of the actual magnetic field and a stress-dependent feedback term. Construction and identification of the model is carried out by providing experimental training data to a modular discrete Hopfield neural network–linear neural network combination. Experimental testing suggests that this model can lead to good qualitative and quantitative simulation results.
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paper_title: Neural network and Fourier descriptor macromodeling dynamic hysteresis

paper_content:

For the evaluation of the dynamic hysteresis loops, a Neural Network (NN) combined with the Fourier Descriptor (FD) technique can be a simple computational instrument alternative to the classical approach. This method is suitable in those cases in which a distorted periodic magnetic field H, or flux density B, excites, in steady state, the ferromagnetic nucleus of a device. The dependence of the hysteresis loop from the magnetic field frequency, has been successfully evaluated by NN, while, by means of the Fourier Descriptor, the effects of the magnetic field distortion have been efficiently predicted. Numerical results compared with those from other models (i.e. Jiles model) and experimental data are presented in the end.
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paper_title: A new anisotropic vector hysteresis model based on stop hysterons

paper_content:

A new anisotropic magnetic hysteresis model based on the Prandtl operators is proposed. This model is suitable to calculate the magnetic vector field H from an imposed vector induction B. The model is easy to implement, requiring little computational effort with a small set of material parameters. The methodology is validated by comparing experimental and calculated results.
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paper_title: Artificial Neural Network Modeling of Mean-Field Ising Hysteresis

paper_content:

In this study, the artificial neural network (ANN) was used to model ferromagnetic Ising hysteresis obtained from mean-field analysis as a case study. ANNs were trained to predict the effect of external perturbations, which are the temperature, the field amplitude and the field frequency, on the hysteresis properties, which are the hysteresis area, the remanence magnetization and the coercivity. The input data to the ANN were split into training data, testing data and validating data. Search were carried out to identify number of hidden layer and number of hidden nodes to find the best architecture with highest accuracy. After the networks had been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the actual data were found to match very well over an extensive range. This therefore suggests a success in modeling ferromagnetic hysteresis properties using the ANN technique.
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paper_title: Comparison of the classical and generalized preisach hysteresis models with experiments

paper_content:

The authors describe the experimental testing of the accuracy of the classical and generalized (nonlinear) Preisach models of hysteresis. The experiments performed for two typical magnetic tape materials show that both models are about equally accurate when the reversal values of the magnetic field are larger than or equal to coercive field values. However, the generalized Preisach model gives more accurate predictions when the reversal values of the magnetic fields are smaller than the coercive values. >
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paper_title: Identification of parameters in an accommodation model

paper_content:

An accommodation model has been recently presented which interprets Preisach-type models from a statistical point of view. In this paper an identification strategy for determining the accommodation parameter, /spl beta/, is presented. This method is applicable for more general Preisach distributions where the standard deviation in the interaction field is not equal to the standard deviation in the critical field. Experimental verification of the properties of the accommodation model using a commercial flexible disk recording media is presented. >
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paper_title: Implementation of magnetostriction Preisach-type models using orthogonally coupled hysteresis operators

paper_content:

Magnetic materials exhibiting gigantic magnetostriction are currently being used in various actuator devices and vibration damping applications. Recently, a new family of efficient Preisach-type vector hysteresis models having coupled elementary operators has been introduced. The purpose of this paper is to extend the applicability of those recently introduced models to magnetostriction simulation. Details of the model, its identification, and experimental testing are presented in the paper.
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paper_title: Hysteresis modelling of anisotropic barium-ferrite

paper_content:

The hysteresis loop of technical barium ferrite was measured at room temperature applying the external field parallel and perpendicular to the preferential axis. The hysteresis loop was analyzed using the energetic as well as the Preisach model. The parameters of the energetic model allow a physical interpretation of the results.
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paper_title: Magnetostriction simulation using anisotropic vector Preisach-type models

paper_content:

Magnetic materials exhibiting gigantic magnetostriction, especially Terfenol, are currently being widely used in fine positioning and active vibration damping devices. By involving accurate magnetostriction models during design stages, precision of such devices may be significantly enhanced. In this paper a straight-forward approach that employs anisotropic vector Preisach-type hysteresis models in simulating field-stress effects on magnetic materials is presented. Formation of the proposed model is given and its identification problem is solved. The presented approach has been numerically implemented and numerous digital computer simulations have been performed for Terfenol material. Sample simulation results as well as comparisons with experimentally observed magnetostriction curves are reported in the paper.
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paper_title: Neural-Preisach-type models and their application to the identification of magnetic hysteresis from noisy data

paper_content:

Abstract A novel class of Preisach-type hysteresis transducers is introduced and their properties are investigated. These transducers can be numerically implemented by using feed-forward neural networks and their identification can be performed by learning algorithms. Numerical examples that provide evidence of the capability of this approach of filtering noise embedded in the data used for identification, are presented.
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paper_title: A new neural-network-based scalar hysteresis model

paper_content:

A neural network (NN)-based model of scalar hysteresis characteristics has been developed for modeling the behavior of magnetic materials. The virgin curve and a set of the first-order reversal branches can be stored preliminary in a system of three NNs. Different properties of magnetic materials can be simulated by a simple if-then type knowledge-based algorithm. Hysteresis characteristics of different materials predicted by the introduced model are compared with the results of the classical Preisach simulation technique. Comparisons are plotted in figures.
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paper_title: New Preisach‐type models of hysteresis and their experimental testing

paper_content:

New Preisach‐type models of scalar hysteresis are discussed and the results concerning experimental testing of these models are given.
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paper_title: Using neural networks in the identification of Preisach-type hysteresis models

paper_content:

The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.
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paper_title: Direct solution method for finite element analysis using Hopfield neural network

paper_content:

One property of the Hopfield neural network is the monotonous minimization of energy as time proceeds. In this paper, this property is applied to minimize the energy functional obtained by ordinary finite element analysis. The mathematical representation and correlation between finite element and neural network calculus are presented. The selection of the sigmoid function and its influence on the iteration process is discussed. The obtained results using the proposed method show excellent agreement with theoretical solutions. >
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paper_title: Automated two-dimensional field computation in nonlinear magnetic media using Hopfield neural networks

paper_content:

It is well known that the computation of magnetic fields in nonlinear magnetic media may be carried out using different approaches. In the case of problems involving complex geometries and/or magnetic media, numerical techniques become especially more appealing. In this paper, we present an automated integral equation approach using which two-dimensional field computations may be carried out in nonlinear magnetic media. This approach is constructed in terms of a continuous Hopfield neural network (HNN) whose neuron activation functions are set to mimic the vectorial magnetic properties of the media. Using well-established HNN energy minimization algorithms, an automated solution of the problem is then obtained. The approach has been implemented and resulted in good agreement with finite-element (FE) computations. Details of the approach, computations, and FE results are given in this paper.
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paper_title: Utilizing hopfield neural networks in the analysis of reluctance motors

paper_content:

Reluctance motors are currently being used widely in different applications. Sometimes, the rotor inherent saliency may introduce some difficulty in pursuing an analytical solution to the motor electromagnetic field problem. In this paper, Hopfield artificial neural networks are used to minimize the air-gap magnetic energy function. Thus, a numerical electromagnetic field solution is obtained automatically. Performance of the motor may then be computed from the obtained field solution. Simulations for a motor having typical dimensions are presented in the paper. It is found that the results of these simulations are in full agreement with reported results as well as well known theoretical aspects.
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paper_title: Numerical Methods in Electromagnetism

paper_content:

1. Basic Principles of Electromagnetic Fields 2. Overview of Computational Methods in Electromagnetics 3. The Finite Difference Method 4. Variational and Galerkin Methods 5. Shape Functions 6. The Finite Element Method 7. Integral Equations 8. Open Boundary Problems 9. High- Frequency Problems with Finite Elements 10. Low-Frequency Applications 11. Solution of Equations A. Vector Operators B. Triangle Area in Terms of Vertex Coordinates C. Fourier Transform Mehtod D. Integrals of Area Coordinates E. Integrals of Voluume Coordinates F. Gauss-Legendre Quadrature Formulae, Abscissae and, Weight Coefficients G. Shape Functions for 1D Finite Elements H. Shape Functions for 2D Finite Elements I. Shape Functions for 3D Finite Elements References Index
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paper_title: On penetration of electromagnetic fields into nonlinear conducting ferromagnetic media

paper_content:

The penetration of plane electromagnetic waves into a ferromagnetic conducting half‐space is analytically investigated. The model used for the permeability is based on a ‘‘nth root approximation’’ for magnetization curve (B=KH1/n,n>1) and does not include hysteresis. In the case of circular polarization of electromagnetic waves, the exact analytical solutions of the nonlinear differential equations are given. Here an interesting effect appears: the solutions are marked by the absence of higher‐order harmonics everywhere within the medium, despite its nonlinear magnetic properties. The case of noncircular polarization of electromagnetic waves is treated as a perturbation of circular polarization. Linear equations for perturbations are then derived and their analytical solutions are found for the cases of elliptical polarizations of magnetic and electric fields, respectively. The results of calculations of surface impedances and third order harmonics of electromagnetic fields are presented. The accuracy of ...
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Title: Utilizing neural networks in magnetic media modeling and field computation: A review

Section 1: Introduction
Description 1: In this section, introduce the importance of magnetic materials in various applications, the complexity of modeling these materials, and the potential of artificial neural networks (ANNs) in simulating magnetic media.

Section 2: Overview of commonly used artificial neural networks in magnetics
Description 2: Provide an overview of the different types of neural networks commonly used in magnetic media applications, including their architectures and learning paradigms.

Section 3: Feed-Forward Neural Networks (FFNN)
Description 3: Describe the structure, functionality, and implementation of Feed-Forward Neural Networks in the context of magnetic media modeling.

Section 4: Continuous Hopfield Neural Networks (CHNN)
Description 4: Outline the characteristics of Continuous Hopfield Neural Networks, their continuous-time operation, and applications in magnetic media.

Section 5: Discrete Hopfield Neural Networks (DHNN)
Description 5: Discuss the structure and implementation of Discrete Hopfield Neural Networks, particularly in modeling hysteresis operators and vector hysteresis models.

Section 6: Hybrid Hopfield Neural Networks (HHNN)
Description 6: Explain the hybrid approach combining continuous and discrete Hopfield networks for smoother hysteresis operator implementation and modeling.

Section 7: Linear Neural Networks (LNN)
Description 7: Present the application of Linear Neural Networks in magnetic media modeling, focusing on their training and optimization algorithms like LMS and Levenberg-Marquardt methods.

Section 8: Modular Neural Networks (MNN)
Description 8: Describe the utilization of Modular Neural Networks for solving specific electromagnetic problems by combining multiple neural network modules.

Section 9: Utilizing neural networks in modeling complex magnetic media
Description 9: Discuss the application of neural networks for modeling the magnetization processes in complex magnetic media, including scalar and vector hysteresis models.

Section 10: Utilizing neural networks in field computation involving nonlinear magnetic media
Description 10: Detail the use of artificial neural networks in numerical field computation, particularly in nonlinear magnetic media, and describe relevant algorithms and methodologies for implementing these computations.