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LMI-based approach for global exponential robust stability for reaction–diffusion uncertain neural networks with time-varying delay

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  • Wang, Linshan
  • Zhang, Yan
  • Zhang, Zhe
  • Wang, Yangfan

Abstract

Global exponential robust stability is considered for a class of reaction–diffusion uncertain neural networks with time-varying delays. The purpose of the problem addressed is to establish some easy-to-test criteria for global exponential robust stability for the uncertain systems by means of a new Lyapunov–Krasovskii functional and a linear matrix inequality (LMI). A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.

Suggested Citation

  • Wang, Linshan & Zhang, Yan & Zhang, Zhe & Wang, Yangfan, 2009. "LMI-based approach for global exponential robust stability for reaction–diffusion uncertain neural networks with time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 41(2), pages 900-905.
    • Handle: RePEc:eee:chsofr:v:41:y:2009:i:2:p:900-905
    DOI: 10.1016/j.chaos.2008.04.020
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    References listed on IDEAS

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    1. Zhou, Qinghua & Wan, Li & Sun, Jianhua, 2007. "Exponential stability of reaction–diffusion generalized Cohen–Grossberg neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1713-1719.
    2. Lou, Xuyang & Cui, Baotong, 2007. "Boundedness and exponential stability for nonautonomous cellular neural networks with reaction–diffusion terms," Chaos, Solitons & Fractals, Elsevier, vol. 33(2), pages 653-662.
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