Efficient implementation of vector Preisach-type models using orthogonally coupled hysteresis operators

Utilizing neural networks in magnetic media modeling and field computation: A review

“…The idea of constructing an elementary rectangular hysteresis operator, using a two-node DHNN, was first demonstrated in [13] . Then, vector hysteresis models have been constructed using two orthogonally-coupled scalar operators (i.e., rectangular loops) [14–16] . Furthermore, an ensemble of octal or, in general, N clusters of coupled step functions has been proposed to efficiently model vector hysteresis as will be discussed in the following sections [17,18] .…”

“…Given different sets of inputs I i , i = 1, … , N and the corresponding outputs O , the linear neuron in Fig. 3 a finds the weight values W 1 through W N such that the mean-square error is minimized [13–16] . In order to determine the appropriate values of the weights, training data is provided to the network and the least-mean-square (LMS) algorithm is applied to the linear neuron.…”

“…Finally, many electromagnetic problems are best solved using neural networks consisting of several modules with sparse interconnections between the modules [11–14,16] . Modularity allows solving small tasks separately using small neural network modules and then combining those modules in a logical manner.…”

“…2 a). More specifically, an efficient vector PM was constructed from only two scalar models having orthogonally inter-related elementary operators was proposed [14] . Such model was implemented via a LNN fed from a four-node DHNN blocks having step activation functions as shown in Fig.…”

“…Sample identification and testing results are shown in Fig. 7 (please refer to [14] ). The approach was further generalized by using HHNN as described in the overview section [19] .…”