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Global Robust Exponential Stability Analysis for Interval Neural Networks with Mixed Delays

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  • Yanke Du
  • Rui Xu

Abstract

A class of interval neural networks with time‐varying delays and distributed delays is investigated. By employing H‐matrix and M‐matrix theory, homeomorphism techniques, Lyapunov functional method, and linear matrix inequality approach, sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point to the neural networks are established and some previously published results are improved and generalized. Finally, some numerical examples are given to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Yanke Du & Rui Xu, 2012. "Global Robust Exponential Stability Analysis for Interval Neural Networks with Mixed Delays," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:647231
    DOI: 10.1155/2012/647231
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    References listed on IDEAS

    as
    1. Arik, Sabri, 2005. "Global robust stability analysis of neural networks with discrete time delays," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1407-1414.
    2. Singh, Vimal, 2007. "Improved global robust stability criterion for delayed neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 31(1), pages 224-229.
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