Abstract
Dynamic magnetic hysteresis modelling is of crucial importance in determining the electromagnetic behaviour of magnetic cores. Among the different models of magnetic hysteresis, Preisach model is one of the more commonly applied. The dynamic Preisach model is a generalisation of the static Preisach model, and is obtained by adding the rate of change of the output variable, which makes the numerical implementation more complex. Since dynamic hysteresis models require mapping of time dependent sequences, a feature available in some neural networks, in this paper the application of recurrent neural networks to model dynamic hysteresis was investigated. An Elman neural network was selected to test the concept. The network was trained with a set of measured dynamic data and was tested with another set of experimental data, showing acceptable accuracy.
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