Nonsmooth training of fuzzy neural networks

Authors:
C. Eitzinger
Affiliations:
Profactor ProduktionsforschungsGmbH, Austria
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2004

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Abstract

The integration of fuzzy methods and neural networks often leads to nonsmoothness of the neural network and, consequently, to a nonsmooth training problem. It is shown, that smooth training methods as e.g. backpropagation fail to converge in this case. Thus a method – based on so called bundle-methods – for training of nonsmooth neural network is presented. Numerical results obtained from a character recognition problem show, that this method still converges where backpropagation fails.