Adaptive Modeling of Highly Nonlinear Hysteresis Using Preisach Neural Networks
Publication: Journal of Engineering Mechanics
Volume 140, Issue 4
Abstract
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.
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© 2014 American Society of Civil Engineers.
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Received: Jan 14, 2013
Accepted: Aug 15, 2013
Published online: Aug 19, 2013
Published in print: Apr 1, 2014
Discussion open until: Jun 10, 2014
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