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Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays

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Abstract

This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete-time stochastic neural networks with interval time-varying delay. By using the linear matrix inequality method and the free-weighting matrix technique, we construct a new Lyapunov–Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete-time stochastic neural networks with interval time-varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete-time stochastic neural networks. Based on the state feedback control law, some novel delay-dependent criteria of the robust exponential stabilization for a class of discrete-time stochastic neural networks with interval time-varying delay are established. The controller gains are designed to ensure the global robust exponential stability of the closed-loop systems. Finally, numerical examples illustrate the effectiveness of the theoretical results we have obtained.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China under Grant 61603272.

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Authors and Affiliations

  1. School of Science, Tianjin Polytechnic University, Tianjin, 300387, China

    Yali Dong, Shuang Liang & Liangliang Guo

Authors
  1. Yali Dong
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  2. Shuang Liang
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  3. Liangliang Guo
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Correspondence to Yali Dong.

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Dong, Y., Liang, S. & Guo, L. Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays. Neural Process Lett 46, 135–158 (2017). https://doi.org/10.1007/s11063-016-9575-1

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  • DOI: https://doi.org/10.1007/s11063-016-9575-1

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