Convergence of BP algorithm for product unit neural networks with exponential weights

Authors:
C. Zhang;W. Wu;X. H. Chen;Y. Xiong
Affiliations:
Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China;Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China;Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China;Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China
Venue:
Neurocomputing
Year:
2008

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Abstract

Product unit neural networks with exponential weights (PUNNs) can provide more powerful internal representation capability than traditional feed-forward neural networks. In this paper, a convergence result of the back-propagation (BP) algorithm for training PUNNs is presented. The monotonicity of the error function in the training iteration process is also guaranteed. A numerical example is given to support the theoretical findings.