Delay-interval-dependent stability of recurrent neural networks with time-varying delay

Authors:
Chuandong Li;Gang Feng
Affiliations:
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310038, China and Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, Chin ...;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper studies the delay-interval-dependent stability of the equilibrium point of a general class of recurrent neural networks with time-varying delays that may exclude zero. By constructing the appropriate Lyapunov-Krasovskii functional, two sufficient conditions ensuring the global asymptotic stability of the equilibrium point of such networks with interval-time-varying delays are established. The present results, together with two numerical examples, show that the equilibrium points of the considered networks may be globally asymptotically stable in some delay interval(s) even though the equilibrium points of the corresponding delay-free recurrent neural networks are not globally asymptotically stable.