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Indexed by:期刊论文

Date of Publication:2008-12-01

Journal:NEUROCOMPUTING

Included Journals:SCIE、EI、Scopus

Volume:72

Issue:1-3

Page Number:513-520

ISSN No.:0925-2312

Key Words:Neural network; Product unit; Exponential weights; Back-propagation algorithm; Convergence

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. (c) 2008 Elsevier B.V. All rights reserved.