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New Criteria on Delay-Dependent Robust Stability for Uncertain Markovian Stochastic Delayed Neural Networks

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Abstract

This paper is mainly concerned about the problem of the robustly exponential stability of uncertain stochastic neural networks with time-varying delay and Markovian jump parameters. Some new delay-dependent stability criteria are established for the considered systems by constructing a modified Lyapunov–rasovskii functional, which are expressed in terms of linear matrix inequalities. Compared with some existing ones, our obtained results have a potential less conservatism. Finally, three illustrative numerical examples are provided to show the effectiveness of the obtained results.

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Acknowledgments

The authors would like to thank the referee and the editor for their careful comments and valuable suggestions on this work. This work was supported by the National Natural Science Foudation of China under Grant No, 61365013, and the Foundation of Jiangxi Provincial Educations of China under Grant No. GJJ14155.

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Authors and Affiliations

  1. School of Science, Nanchang University, Nanchang , 330031, Jiangxi, People’s Republic of China

    Huabin Chen, Jinjing Wang & Liu Wang

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  1. Huabin Chen
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  2. Jinjing Wang
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  3. Liu Wang
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Correspondence to Huabin Chen.

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Chen, H., Wang, J. & Wang, L. New Criteria on Delay-Dependent Robust Stability for Uncertain Markovian Stochastic Delayed Neural Networks. Neural Process Lett 42, 275–290 (2015). https://doi.org/10.1007/s11063-014-9356-7

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