On global asymptotic stability for a class of delayed neural networks

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

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Detail(s)

Original languageEnglish
Pages (from-to)1165-1174
Journal / PublicationInternational Journal of Circuit Theory and Applications
Volume40
Issue number11
Publication statusPublished - Nov 2012

Abstract

This paper deals with the problem of stability analysis for a class of delayed neural networks described by nonlinear delay differential equations of the neutral type. A new and simple sufficient condition guaranteeing the existence, uniqueness and global asymptotic stability of an equilibrium point of such a kind of delayed neural networks is developed by the Lyapunov-Krasovskii method. The condition is expressed in terms of a linear matrix inequality, and thus can be checked easily by recently developed standard algorithms. When the stability condition is applied to the more commonly encountered delayed neural networks, it is shown that our result can be less conservative. Examples are provided to demonstrate the effectiveness of the proposed criteria. Copyright © 2011 John Wiley & Sons, Ltd..

Research Area(s)

  • global asymptotic stability, linear matrix inequality, neural networks, neutral systems, time-delay systems