On the basis of the Kolmogorov‐Arnold theory, the feedforward type artificial neural networks (NNs) are able to approximate any kind of nonlinear, continuous functions represented by its discrete set of measurements. A NN‐based scalar hysteresis model has been constructed preliminarily on the function approximation ability of NNs. An if‐then type knowledge‐base represents the properties of the hysteresis characteristics. Vectorial generalization to describe isotropic and anisotropic magnetic materials in two and three dimensions with an original identification method has been introduced in this paper.
Kuczmann, M. and Iványi, A. (2003), "Vector hysteresis model based on neural network", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 22 No. 3, pp. 730-743. https://doi.org/10.1108/03321640310475155Download as .RIS
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