Adaptive Modeling of Highly Nonlinear Hysteresis Using Preisach Neural Networks.

In this paper, a new type of multilayer feedforward neural network has been proposed based on inspiration from the Preisach model, which has been called the Preisach neural network (Preisach-NN). It is comprised of input, output, and two hidden layers. The input and output layers contain linear neurons, whereas the first hidden layer incorporates neurons called stop neurons, whose activation function represents a stop operator. The second hidden layer includes sigmoidal neurons. The subgradient optimization method with space dilatation has been adopted for training of the Preisach-NN as a nonsmooth problem. Although the proposed Preisach-NN could be mathematically identical to the Preisach model, tuning of the Preisach-NN is easier and also more general than that of the model. To assess their capability, Preisach-NNs are used to model two different types of hysteretic behaviors of Masing and non-Masing problems. The results presented and discussed in this paper show that the neural networks have been capable of learning the material behaviors successfully and with high precision. [ABSTRACT FROM AUTHOR]