Title: New delay-dependent stability criterion for stochastic recurrent neural networks

Authors: Liyun Yang, Mifeng Ren, Dongbo Hao, Liqin Yang

Addresses: College of Science, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China. ' College of Science, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China. ' Hebei Poshing Electronics Technology Co., Ltd., Shijiazhuang, Hebei 050200, China. ' Chengezhuang Middle School, Qinhuangdao, Hebei 066600, China

Abstract: In this paper, the problem of delay-dependent robust stability for uncertain stochastic neural networks with time-varying delay is considered. Based on Lyapunov stability theory combined with linear matrix inequalities (LMI) techniques, some new delay-dependent stability criteria in terms of LMI are derived by introducing some free weighing matrices and using Leibniz-Newton formula which can be selected properly to lead to less conservative results. Finally, two examples are given. One is given to illustrate, the other is an extended model for prevenient systems.

Keywords: stochastic recurrent neutral networks; time-varying delay; delay-dependent; robust stability; time delays; Lyapunov stability theory; linear matrix inequalities; LMI.

DOI: 10.1504/IJMIC.2010.037026

International Journal of Modelling, Identification and Control, 2010 Vol.11 No.3/4, pp.164 - 172

Available online: 21 Nov 2010 *

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