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Stochastic stability analysis for neural networks with mixed time-varying delays

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Abstract

This paper is concerned with the problem of the stochastic stability analysis for Markovian jumping neural networks with time-varying delays and stochastic perturbation. Some criteria for the stability and robust stability of such neural networks are derived, by means of constructing suitable Lyapunov–Krasovskii functionals and a unified linear matrix inequality (LMI) approach. Note that the LMIs can be easily solved by using the Matlab LMI toolbox and no tuning of parameters is required. Finally, numerical examples are used to illustrate the effectiveness and advantage of the proposed techniques.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (No. 61273004) and the Natural Science Foundation of Hebei Province (No. F2014203085). The authors are grateful to the chief editor, and the anonymous referees for their careful reading and constructive comments and valuable suggestions, which helped improving the presentation of the Letter.

Conflict of interest

We hereby confirm that this manuscript is our original work and has not been published nor has it been submitted simultaneously elsewhere. We further confirm that all authors have checked the manuscript and have agreed to the submission. Moreover, we declare that there is no conflict of interests regarding the publication of this article.

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  1. College of Science, Yanshan University, Qinhuangdao, Hebei Province, China

    Yuechao Ma & Yuqing Zheng

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  1. Yuechao Ma
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  2. Yuqing Zheng
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Correspondence to Yuqing Zheng.

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Ma, Y., Zheng, Y. Stochastic stability analysis for neural networks with mixed time-varying delays. Neural Comput & Applic 26, 447–455 (2015). https://doi.org/10.1007/s00521-014-1735-5

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