Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

Abstract

A modeling technique for rate-independent ~quasistatic! scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at… (More)

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@inproceedings{Makaveev2001ModelingOQ, title={Modeling of quasistatic magnetic hysteresis with feed-forward neural networks}, author={Dimitre Makaveev and Luc Dupr{\'e} and Marc De Wulf and Jan A. Melkebeek}, year={2001} }