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Indexed by:期刊论文

Date of Publication:2007-03-01

Journal:Journal of Information and Computational Science

Included Journals:EI

Volume:4

Issue:1

Page Number:251-255

ISSN No.:15487741

Abstract:Gradient algorithm has been widely used as a learning algorithm for training the weights of feedforward neural networks. A simple criterion is used to choose the learning rate during each cycle of training iteration. A convergence result of the corresponding batch gradient algorithm and the monotonicity error function in the iteration are proved, which are stronger than the usual results for general optimization problems. A supporting numerical experiment is also presented.