Abstract
A neural network (NN) model of scalar hysteresis phenomena has been developed for modeling the behavior of magnetic materials. The function approximation ability of NNs has been applied. 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 knowledge-based algorithm. Value of differential susceptibility, dM/dH can be expressed in analytical form. Finally hysteresis characteristics predicted by the introduced model are compared with the results of the Preisach simulation technique.