Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)447-448
Journal / PublicationElectronics Letters
Volume39
Issue number5
Publication statusPublished - 6 Mar 2003

Abstract

An approach for the improved performance of back-propagation (BP) learning systems, based on the integration of magnified gradient function and weight evolution with deterministic perturbation, was discussed. In this regard, the regression and character recognition problems were considered. The simulation results, in terms of the convergence rate and global convergence, showed that the integrated approach always outperformed other traditional methods.