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Indexed by:期刊论文

Date of Publication:2008-09-01

Journal:APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION

Included Journals:SCIE、EI、Scopus

Volume:29

Issue:9

Page Number:1231-1238

ISSN No.:0253-4827

Key Words:Elman network; gradient learning algorithm; convergence; monotonicity

Abstract:The gradient method for training Elman networks with a finite training sample set is considered. Monotonicity of the error function in the iteration is shown. Weak and strong convergence results are proved, indicating that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. A numerical example is given to support the theoretical findings.