Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays

Authors:
Feiqi Deng;Mingang Hua;Xinzhi Liu;Yunjian Peng;Juntao Fei
Affiliations:
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, PR China;College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, PR China and Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Ca ...;Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1;College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, PR China;College of Computer and Information, Hohai University, Changzhou 213022, PR China
Venue:
Neurocomputing
Year:
2011

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Abstract

This paper is concerned with the robust delay-dependent exponential stability of uncertain stochastic neural networks (SNNs) with mixed delays. Based on a novel Lyapunov-Krasovskii functional method, some new delay-dependent stability conditions are presented in terms of linear matrix inequalities, which guarantee the uncertain stochastic neural networks with mixed delays to be robustly exponentially stable. Numerical examples are given to illustrate the effectiveness of our results.