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论文类型：期刊论文

发表时间：2012-07-15

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：89

页面范围：141-146

ISSN号：0925-2312

关键字：Feedforward neural networks; Batch back-propagation algorithm; Penalty; Boundedness; Convergence

摘要：This paper investigates the batch back-propagation algorithm with penalty for training feedforward neural networks. A usual penalty is considered, which is a term proportional to the norm of the weights. The learning rate is set to be a small constant or an adaptive series. The main contribution of this paper is to theoretically prove the boundedness of the weights in the network training process. This boundedness is then used to prove some convergence results of the algorithm, which cover both the weak and strong convergence. Simulation results are given to support the theoretical findings. (c) 2012 Elsevier B.V. All rights reserved.