Robust exponential stability for uncertain stochastic neural networks with discrete and distributed time-varying delays |
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Authors: | Hongyi Li Bing Chen Qi Zhou Shengle Fang |
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Affiliation: | a Department of Mathematics, Bohai University, Jinzhou, Liaoning 121000, PR China b Institute of Complexity Science, Qingdao University, Qingdao 266071, PR China c Institute of Nonlinear Complex Systems, China Three Gorges University, Yichang, Hubei 443002, PR China |
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Abstract: | This Letter deals with the problem of delay-dependent robust exponential stability in mean square for a class of uncertain stochastic Hopfield neural networks with discrete and distributed time-varying delays. Based on Lyapunov-Krasovskii functional and the stochastic stability theory, delay-dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Because of introducing some free-weighting matrices to develop the stability criteria, the proposed stability conditions have less conservatism. Numerical examples are given to illustrate the effectiveness of our results. |
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Keywords: | Neural networks Exponential stability Stochastic systems Uncertain systems LMIs |
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