HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Xiong, Y. Articles by Zhang, C. Search for Related Content PubMed Articles by Xiong, Y. Articles by Zhang, C.

Training Pi-Sigma Network by Online Gradient Algorithm with Penalty for Small Weight Update

xiongyan888{at}sohu.com Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, People's Republic of China, and Faculty of Science, University of Science and Technology Liaoning, Anshan, 114051, People's Republic of China

Wei Wu

wuweiw{at}dlut.edu.cn Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, People's Republic of China

Xidai Kang

kxd_005{at}163.com Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, People's Republic of China

Chao Zhang

zhangchao_fox{at}163.com Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, People's Republic of China

A pi-sigma network is a class of feedforward neural networks with product units in the output layer. An online gradient algorithm is the simplest and most often used training method for feedforward neural networks. But there arises a problem when the online gradient algorithm is used for pi-sigma networks in that the update increment of the weights may become very small, especially early in training, resulting in a very slow convergence. To overcome this difficulty, we introduce an adaptive penalty term into the error function, so as to increase the magnitude of the update increment of the weights when it is too small. This strategy brings about faster convergence as shown by the numerical experiments carried out in this letter.