NEURAL NETWORK MODEL FOR SCALAR AND VECTOR HYSTERESIS
Miklós Kuczmann  Amália Iványi
The classical Preisach model is one of the most generally applied techniques to simulate the behaviour of magnetic
materials, to describe hysteresis phenomena. According to the theory of Weiss, the classical Preisach model assumes that
ferromagnetic materials consist of many elementary interacting domains, and each of them can be represented by a rectangular
elementary hysteresis loop. The fundamental concepts of the Preisach model is that different domains have some probability,
which can be described by a distribution function, also called the Preisach kernel. On the basis of the socalled
KolmogorovArnold theory the feedforward type artificial neural networks are able to approximate any kind of nonlinear,
continuous functions represented by their discrete set of measurements. A neural network (NN) based scalar hysteresis
model has been constructed on the function approximation ability of NNs and ifthen type rules about hysteresis phenomena.
Vectorial generalization to describe isotropic and anisotropic magnetic materials in two and three dimensions with an
original identification method has been introduced in this paper. Good agreement is found between simulated and
experimental data and the results are illustrated in figures.
Keywords: hysteresis characteristics, Everett surface, vector hysteresis, feedforward type neural networks, backpropagation training method
