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Global exponential stability of stochastic memristor-based complex-valued neural networks with time delays

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Abstract

In recent years, the dynamic behaviors of complex-valued neural networks have been extensively investigated in a variety of areas. This paper focuses on the stability of stochastic memristor-based complex-valued neural networks with time delays. By using the Lyapunov stability theory, Halanay inequality and Itô formula, new sufficient conditions are obtained for ensuring the global exponential stability of the considered system. Moreover, the obtained results not only generalize the previously published corresponding results as special cases for our results, but also can be checked with the parameters of system itself. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.

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Acknowledgements

This work was supported by the Key Program of National Natural Science Foundation of China with Grant No. 61134012, National Natural Science Foundation of China with Grant No. 61203055, 11271146, the Postdoctoral Science Foundation of China 2014M560459.

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Authors and Affiliations

  1. School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China

    Dan Liu, Song Zhu & Wenting Chang

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  1. Dan Liu
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  2. Song Zhu
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  3. Wenting Chang
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Correspondence to Song Zhu.

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Liu, D., Zhu, S. & Chang, W. Global exponential stability of stochastic memristor-based complex-valued neural networks with time delays. Nonlinear Dyn 90, 915–934 (2017). https://doi.org/10.1007/s11071-017-3702-z

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  • DOI: https://doi.org/10.1007/s11071-017-3702-z

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