Genetic algorithms and neural networks generalizing the Jiles-Atherton model of static hysteresis for dynamic loops

Abstract

This paper presents a method based on genetic algorithms and neural networks suitable for finding the five parameters of the Jiles-Atherton (JA) model for generalization to dynamic hysteresis loops. The aim is to obtain an equivalent static model for dynamic loops by updating its parameters varying the frequency of the imposed magnetic field H(t). Validations of the present approach compared to other numerical approaches, based on adding frequency-dependent losses to the static model, and versus experimental tests will be shown.