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Exponential state estimation for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions

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Abstract

This paper is concerned with the exponential state estimation issue for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions. By introducing triple-integral terms and quadruple integrals term in Lyapunov–Krasovskii functional, the obtained Lyapunov matrices are distinct for different system modes. Based on the nonsmooth analysis theory and by applying stochastic analysis techniques, the full-order state estimator is designed to ensure that the corresponding error system is exponentially stable in mean square. The desired mode-dependent and delay-dependent estimator can be achieved by solving a set of linear matrix inequalities. Finally, two simulation examples are given to illustrate the validity of the theoretical results.

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Acknowledgments

The authors are extremely grateful to anonymous reviewers for their careful reading of the manuscript and insightful comments, which help to enrich the content. We would also like to acknowledge the valuable comments and suggestions from the editors, which vastly contributed to improve the presentation of this paper. This work was jointly supported by the National Natural Science Foundation of China (61573306), the Postgraduate Innovation Project of Hebei province of China (00302-6370019) and the Natural Science Foundation of Hebei Province of China (A2011203103).

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Authors and Affiliations

  1. School of Science, Yanshan University, 066001, Qinhuangdao, China

    Huaiqin Wu, Leifei Wang & Yu Wang

  2. School of Electrical Engineering, Yanshan University, 066001, Qinhuangdao, China

    Peifeng Niu

  3. School of Information Science and Engineering, Yanshan University, 066001, Qinhuangdao, China

    Bolin Fang

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  1. Huaiqin Wu
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  2. Leifei Wang
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  3. Yu Wang
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  4. Peifeng Niu
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  5. Bolin Fang
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Correspondence to Huaiqin Wu.

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Wu, H., Wang, L., Wang, Y. et al. Exponential state estimation for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions. Int. J. Mach. Learn. & Cyber. 7, 641–652 (2016). https://doi.org/10.1007/s13042-015-0447-1

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  • DOI: https://doi.org/10.1007/s13042-015-0447-1

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