Multiple‐interval‐dependent robust stability analysis for uncertain stochastic neural networks with mixed‐delays |
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| Authors: | Jianwei Xia Ju H Park Hao Shen |
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| Affiliation: | 1. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea;2. School of Mathematics Science, Liaocheng University, Shandong, China;3. School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, China |
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| Abstract: | This article deals with the problem of robust stochastic asymptotic stability for a class of uncertain stochastic neural networks with distributed delay and multiple time‐varying delays. It is noted that the reciprocally convex approach has been intensively used in stability analysis for time‐delay systems in the past few years. We will extend the approach from deterministic time‐delay systems to stochastic time‐delay systems. And based on the new technique dealing with matrix cross‐product and multiple‐interval‐dependent Lyapunov–Krasovskii functional, some novel delay‐dependent stability criteria with less conservatism and less decision variables for the addressed system are derived in terms of linear matrix inequalities. At last, several numerical examples are given to show the effectiveness of the results. © 2014 Wiley Periodicals, Inc. Complexity 21: 147–162, 2015 |
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| Keywords: | stochastic neural networks stochastic stability distributed delay multiple‐delays |
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