Abstract
This paper presents a method based on both artificial neural networks (ANNs) and on a multidimensional optimization procedure in order to significantly reduce the time taken and to improve the accuracy in evaluating parameters of the Jiles-Atherton model of magnetic hysteresis. The main steps of the method are (1) data acquisition of the experimental hysteresis loop of the magnetic material under test, (2) evaluation of the model's parameters by means of ANN, and (3) parameter accuracy improvement by means of a multidimensional optimization procedure. In order to highlight the method's effectiveness, the results of numerical and experimental tests are also given.
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