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Robust exponential stability and domains of attraction in a class of interval neural networks

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  • Yang, Xiaofan
  • Liao, Xiaofeng
  • Bai, Sen
  • Evans, David J

Abstract

This paper addresses robust exponential stability as well as domains of attraction in a class of interval neural networks. A sufficient condition for an equilibrium point to be exponentially stable is established. And an estimate on the domains of attraction of exponentially stable equilibrium points is presented. Both the condition and the estimate are formulated in terms of the parameter intervals, the neurons’ activation functions and the equilibrium point. Hence, they are easily checkable. In addition, our results neither depend on monotonicity of the activation functions nor on coupling conditions between the neurons. Consequently, these results are of practical importance in evaluating the performance of interval associative memory networks.

Suggested Citation

  • Yang, Xiaofan & Liao, Xiaofeng & Bai, Sen & Evans, David J, 2005. "Robust exponential stability and domains of attraction in a class of interval neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 26(2), pages 445-451.
    • Handle: RePEc:eee:chsofr:v:26:y:2005:i:2:p:445-451
    DOI: 10.1016/j.chaos.2004.12.041
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    Cited by:

    1. Park, Ju H., 2007. "An analysis of global robust stability of uncertain cellular neural networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 800-807.
    2. Li, Chuandong & Chen, Jinyu & Huang, Tingwen, 2007. "A new criterion for global robust stability of interval neural networks with discrete time delays," Chaos, Solitons & Fractals, Elsevier, vol. 31(3), pages 561-570.

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