Neural-Preisach-type models and their application to the identification of magnetic hysteresis from noisy data

Visone, C.; Serpico, C.; Mayergoyz, I.D.; Huang, Minqi; Adly, A.A.

doi:10.1016/s0921-4526(99)00764-4

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…The method has to be tested with noisy measurements. Noisy measurements can be treated by neural networks also [8]. Other magnetic materials have to be tested before including this model into computer aided design (CAD) softwares.…”

Section: Resultsmentioning

confidence: 99%

Quasistatic hysteresis modeling with feed-forward neural networks: Influence of the last but one extreme values

Sixdenier

Scorretti

Marion

et al. 2008

Journal of Magnetism and Magnetic Materials

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Section: Resultsmentioning

confidence: 99%

Quasistatic hysteresis modeling with feed-forward neural networks: Influence of the last but one extreme values

Sixdenier

Scorretti

Marion

et al. 2008

Journal of Magnetism and Magnetic Materials

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“…2. Higher order features of can be defined (e.g., second-, third-and th-order reversal curves) [26], [27], but the approximation for the major hysteresis represents a mandatory step to be taken by any identification method with arbitrary precision.…”

Section: The Pwl Approach To the -Approximation For Static Hystermentioning

confidence: 99%

“…Let us consider (22) According to Definition 5 (23) at any time instant , and as , (23) leads to the inequality (24) Equation (20) and Theorem 2 yield (25) and (21), Theorem 3, and Corollary 1 result in (26) Thus, (24) can be rewritten as (27) and…”

Section: The Pwl Approach To the -Approximation For Static Hystermentioning

confidence: 99%

A PWL circuit approach to the definition of a ε-approximation model of scalar static hysteresis

Cincotti

Daneri

2002

IEEE Trans. Circuits Syst. I

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A piecewise-linear (PWL) circuit model of hysteresis phenomena is presented. The proposed model involves the use of parallel-connected simple PWL circuits. The PWL characteristic of the model allows a direct determination of the condition on a rate-independent hysteresis behavior. The basic features of the model are discussed through proper demonstrations. In particular, it is shown that the parameters of the PWL circuit model can be set by using an identification theorem that ensures an arbitrary precision ( -approximation). The theorem results from the generalization of analogous theorems for single-valued functions to many-valued functions. Finally, both numerical comparisons with other models and experimental fittings are presented to verify the robustness of the -approximation theorem to finite-precision arithmetic.

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“…Some hysteresis models based on the theory of NNs have been developed for the last two or three years (Adly and Abd-El-Hafiz, 1998) (Adly et al, 2000) (Serpico and Visone, 1998) (Visone et al, 1999). The memory storage mechanism of magnetic materials can be modelled easily by the Preisach triangle (Ivanyi, 1997) (Mayergoyz, 1991).…”

Section: Introductionmentioning

confidence: 99%

“…The authors of papers (Serpico and Visone, 1998) and (Visone et al, 1999) used a discrete set of Play operators Cðt; h ð jÞ c Þ, where j ¼ 0; . .…”

Section: Introductionmentioning

confidence: 99%

Neural network model of magnetic hysteresis

Kuczmann¹,

Iványi²

2002

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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The classical Preisach model and its modifications are one of the most generally applied simulations to model the behavior of magnetic materials, to describe hysteresis phenomena and different properties, as noncongruent minor loops, frequency dependence, temperature dependence, accommodation, and so on. Artificial neural networks (NNs) are widely used in fields of research where the solution of problems with conventional methods on traditional computers is very difficult to work out, for example system identification, modeling and function approximation. NNs can be considered as universal approximation for functions based on the theorem of Kolmogorov-Arnold. In this paper a new NN model of scalar hysteresis characteristics is introduced. The examined method is built on the function approximation and continuous interpolation capability of NNs. The anhysteretic magnetization curve and a set of the ascending and a set of the descending first order reversal branches can be stored in a system of three neural networks. Different properties of magnetic materials can be simulated by a simple knowledge-based algorithm. Value of differential susceptibility can be expressed in analytical form. Finally hysteresis characteristics predicted by the introduced model are compared with the results of the Preisach simulation.

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Neural-Preisach-type models and their application to the identification of magnetic hysteresis from noisy data

Cited by 14 publications

References 8 publications

Quasistatic hysteresis modeling with feed-forward neural networks: Influence of the last but one extreme values

Quasistatic hysteresis modeling with feed-forward neural networks: Influence of the last but one extreme values

A PWL circuit approach to the definition of a ε-approximation model of scalar static hysteresis

Neural network model of magnetic hysteresis

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