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Adaptive Modeling of Highly Nonlinear Hysteresis Using Preisach Neural Networks.
- Source :
- Journal of Engineering Mechanics; Apr2014, Vol. 140 Issue 4, p-1, 4p
- Publication Year :
- 2014
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 07339399
- Volume :
- 140
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Engineering Mechanics
- Publication Type :
- Academic Journal
- Accession number :
- 94939427
- Full Text :
- https://doi.org/10.1061/(ASCE)EM.1943-7889.0000700