Abstract
A general neural approach to magnetic hysteresis modeling is proposed. The general memory storage properties of systems with rate independent hysteresis are outlined. Thus, it is shown how it is possible to build a neural hysteresis model based on feed-forward neural networks (NN's) which fulfills these properties. The identification of the model consists in training the NN's by usual training algorithms such as backpropagation. Finally, the proposed neural model has been tested by comparing its predictions with experimental data.
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