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Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays |
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| Authors: | Feiqi DengAuthor Vitae Mingang HuaAuthor Vitae Xinzhi LiuAuthor Vitae Yunjian PengAuthor VitaeJuntao FeiAuthor Vitae |
| Affiliation: | a College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, PR China b Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 c College of Computer and Information, Hohai University, Changzhou 213022, PR China |
| 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. |
| Keywords: | Stochastic neural networks Exponential stability Linear matrix inequality (LMI) |
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