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Dynamical Behaviors of Stochastic Hopfield Neural Networks with Both Time‐Varying and Continuously Distributed Delays

Author

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  • Qinghua Zhou
  • Penglin Zhang
  • Li Wan

Abstract

This paper investigates dynamical behaviors of stochastic Hopfield neural networks with both time‐varying and continuously distributed delays. By employing the Lyapunov functional theory and linear matrix inequality, some novel criteria on asymptotic stability, ultimate boundedness, and weak attractor are derived. Finally, an example is given to illustrate the correctness and effectiveness of our theoretical results.

Suggested Citation

  • Qinghua Zhou & Penglin Zhang & Li Wan, 2013. "Dynamical Behaviors of Stochastic Hopfield Neural Networks with Both Time‐Varying and Continuously Distributed Delays," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:631734
    DOI: 10.1155/2013/631734
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    References listed on IDEAS

    as
    1. Song, Qiankun & Zhao, Zhenjiang, 2005. "Global dissipativity of neural networks with both variable and unbounded delays," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 393-401.
    2. Lou, Xu Yang & Cui, Bao Tong, 2008. "Global robust dissipativity for integro-differential systems modeling neural networks with delays," Chaos, Solitons & Fractals, Elsevier, vol. 36(2), pages 469-478.
    3. Zhao, Hongyong & Ding, Nan & Chen, Ling, 2009. "Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1653-1659.
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