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LMI conditions for stability of stochastic recurrent neural networks with distributed delays

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  • Rakkiyappan, R.
  • Balasubramaniam, P.

Abstract

In this paper, the global asymptotic stability of stochastic recurrent neural networks with discrete and distributed delays is analyzed by utilizing the Lyapunov–Krasovskii functional and combining with the linear matrix inequality (LMI) approach. A new sufficient condition ensuring the global asymptotic stability for delayed recurrent neural networks is obtained in the stochastic sense using the powerful MATLAB LMI Toolbox. In addition, an example is also provided to illustrate the applicability of the result.

Suggested Citation

  • Rakkiyappan, R. & Balasubramaniam, P., 2009. "LMI conditions for stability of stochastic recurrent neural networks with distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1688-1696.
    • Handle: RePEc:eee:chsofr:v:40:y:2009:i:4:p:1688-1696
    DOI: 10.1016/j.chaos.2007.09.052
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    References listed on IDEAS

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    1. Zhang, Qiang & Wei, Xiaopeng & Xu, Jin, 2009. "Global exponential stability for nonautonomous cellular neural networks with unbounded delays," Chaos, Solitons & Fractals, Elsevier, vol. 39(3), pages 1144-1151.
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    Cited by:

    1. R. Sakthivel & R. Samidurai & S. M. Anthoni, 2010. "Asymptotic Stability of Stochastic Delayed Recurrent Neural Networks with Impulsive Effects," Journal of Optimization Theory and Applications, Springer, vol. 147(3), pages 583-596, December.
    2. Feng, Wei & Yang, Simon X. & Wu, Haixia, 2009. "On robust stability of uncertain stochastic neural networks with distributed and interval time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2095-2104.

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