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Indexed by:期刊论文

Date of Publication:2007-03-01

Journal:WSEAS Transactions on Mathematics

Included Journals:EI

Volume:6

Issue:3

Page Number:469-476

ISSN No.:11092769

Abstract:Penalty methods have been commonly used to improve the generalization of neural networks and to control the magnitude of network weights. Weight boundedness and convergence results are presented for the online gradient method with penalty for training a single-layer neural network. The monotonicity of the new error function during the training iteration is also proved. Finally, we apply the algorithm to a pattern classification problem to illustrate our theoretical findings.