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Stability analysis of stochastic memristor-based recurrent neural networks with mixed time-varying delays

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Abstract

In this paper, the stability problem of stochastic memristor-based recurrent neural networks with mixed time-varying delays is investigated. Sufficient conditions are established in terms of linear matrix inequalities which can guarantee that the stochastic memristor-based recurrent neural networks are asymptotically stable and exponentially stable in the mean square, respectively. Two examples are given to demonstrate the effectiveness of the obtained results.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 61273120.

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Zhendong Meng & Zhengrong Xiang

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  1. Zhendong Meng
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  2. Zhengrong Xiang
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Correspondence to Zhengrong Xiang.

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Meng, Z., Xiang, Z. Stability analysis of stochastic memristor-based recurrent neural networks with mixed time-varying delays. Neural Comput & Applic 28, 1787–1799 (2017). https://doi.org/10.1007/s00521-015-2146-y

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  • DOI: https://doi.org/10.1007/s00521-015-2146-y

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