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The globally asymptotic stability analysis for a class of recurrent neural networks with delays

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Abstract

This paper considers the problem of global stability of neural networks with delays. By combining Lie algebra and the Lyapunov function with the integral inequality technique, we analyze the globally asymptotic stability of a class of recurrent neural networks with delays and give an estimate of the exponential stability. A few new sufficient conditions and criteria are proposed to ensure globally asymptotic stability of the equilibrium point of the neural networks. A few simulation examples are presented to demonstrate the effectiveness of the results and to improve feasibility.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (50975059/61005080), the Doctoral Foundation of China (20100480994), the Doctoral Foundation of Heilongjiang Province, and the“111” Project (B07018).

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Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China

    Xingguo Song, Haibo Gao, Liang Ding & Minghui Hao

  2. Yanshan University, Qinhuangdao, 066004, China

    Deyou Liu

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  1. Xingguo Song
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  2. Haibo Gao
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  3. Liang Ding
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  4. Deyou Liu
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  5. Minghui Hao
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Correspondence to Xingguo Song.

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Song, X., Gao, H., Ding, L. et al. The globally asymptotic stability analysis for a class of recurrent neural networks with delays. Neural Comput & Applic 22, 587–595 (2013). https://doi.org/10.1007/s00521-012-0888-3

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  • DOI: https://doi.org/10.1007/s00521-012-0888-3

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