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论文类型：期刊论文

发表时间：2008-12-01

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：72

期号：1-3

页面范围：513-520

ISSN号：0925-2312

关键字：Neural network; Product unit; Exponential weights; Back-propagation algorithm; Convergence

摘要：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.