Genetic algorithms and neural networks generalizing the Jiles-Atherton model of static hysteresis for dynamic loops
作者： A. Salvini , F.R. Fulginei
DOI: 10.1109/20.996225
关键词: Hysteresis 、 Generalization 、 Magnetic hysteresis 、 Computer science 、 Artificial neural network 、 Jiles-Atherton model 、 Topology 、 Magnetic field
摘要: This paper presents a method based on genetic algorithms and neural networks suitable for finding the five parameters of Jiles-Atherton (JA) model generalization to dynamic hysteresis loops. The aim is obtain an equivalent static loops by updating its varying frequency imposed magnetic field H(t). Validations present approach compared other numerical approaches, adding frequency-dependent losses model, versus experimental tests will be shown.
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