Mean square stability for stochastic neural networks with distributed and interval time-varying delays

Authors:
Haixia Wu;Wei Feng;Wei Zhang;Songjian Dan
Affiliations:
Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing, China and Department of Further Education, Chongqing Education College, Chongqing, China;Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing, China;Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing, China;Department of Further Education, Chongqing Education College, Chongqing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

This paper is concerned with the asymptotic stability analysis problem for stochastic neural network with distributed and interval time-varying delays. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges of delays, a new delay-range-dependent stability criterion is established to guarantee the delayed neural networks to be asymptotically stable in the mean square. A numerical example has also been used to demonstrate the usefulness of the main result.