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Mean square input-to-state stability of a general class of stochastic recurrent neural networks with Markovian switching

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Abstract

The paper presents M-matrix algebraic criteria for the input-to-state stability of a class of stochastic recurrent neural networks with Markovian switching. First, the criterion without the time delays is derived. Then, the result is extended to time-varying condition. The criterion also ensures globally exponential stability if there is no input term. These conditions are the improvement and extension of the existing results in the literature. A numerical example is given to demonstrate the effectiveness of the proposed algebraic criteria.

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Acknowledgments

This work was supported by the National Science Foundation of China with Grant Nos. 11101434, 61203055 and the Fundamental Research Funds for the Central Universities 2012QNA48.

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Authors and Affiliations

  1. School of Mathematical Sciences and Computing Technology, Central South University, Changsha, 410075, China

    Yong Xu

  2. College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China

    Weiwei Luo, Kai Zhong & Song Zhu

  3. School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Song Zhu

Authors
  1. Yong Xu
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  2. Weiwei Luo
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  3. Kai Zhong
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  4. Song Zhu
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Correspondence to Yong Xu or Song Zhu.

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Xu, Y., Luo, W., Zhong, K. et al. Mean square input-to-state stability of a general class of stochastic recurrent neural networks with Markovian switching. Neural Comput & Applic 25, 1657–1663 (2014). https://doi.org/10.1007/s00521-014-1649-2

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  • DOI: https://doi.org/10.1007/s00521-014-1649-2

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