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Robust Output Feedback Stabilization for Uncertain Discrete-Time Stochastic Neural Networks with Time-Varying Delay

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Abstract

This paper investigates the problem of robust exponential stabilization of uncertain discrete-time stochastic neural networks with time-varying delay based on output feedback control. By choosing an augmented Lyapunov–Krasovskii functional, we established the sufficient conditions of the delay-dependent asymptotical stabilization in the mean square for a class of discrete-time stochastic neural networks with time-varying delay. Furthermore, we obtain the criteria of robust global exponential stabilization in the mean square for uncertain discrete-time stochastic neural networks with time-varying delay. Finally, we give numerical examples to illustrate the effectiveness of the proposed results.

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Acknowledgements

This work was supported by the Natural Science Foundation of Tianjin under Grant No. 18JCYBJC88000 and the National Nature Science Foundation of China under Grant Nos. 61873186,61603272 and 61703307.

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Authors and Affiliations

  1. School of Mathematical Sciences, Tianjin Polytechnic University, Tianjin, 300387, China

    Yali Dong & Huimin Wang

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  1. Yali Dong
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  2. Huimin Wang
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Correspondence to Yali Dong.

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Dong, Y., Wang, H. Robust Output Feedback Stabilization for Uncertain Discrete-Time Stochastic Neural Networks with Time-Varying Delay. Neural Process Lett 51, 83–103 (2020). https://doi.org/10.1007/s11063-019-10077-x

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