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Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time‐Varying Delay

Author

Listed:
  • Wenguang Luo
  • Xiuling Wang
  • Yonghua Liu
  • Hongli Lan

Abstract

The problem of global exponential stability for recurrent neural networks with time‐varying delay is investigated. By dividing the time delay interval [0, τ(t)] into K + 1 dynamical subintervals, a new Lyapunov‐Krasovskii functional is introduced; then, a novel linear‐matrix‐inequality (LMI‐) based delay‐dependent exponential stability criterion is derived, which is less conservative than some previous literatures (Zhang et al., 2005; He et al., 2006; and Wu et al., 2008). An illustrate example is finally provided to show the effectiveness and the advantage of the proposed result.

Suggested Citation

  • Wenguang Luo & Xiuling Wang & Yonghua Liu & Hongli Lan, 2013. "Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time‐Varying Delay," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:540951
    DOI: 10.1155/2013/540951
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    References listed on IDEAS

    as
    1. Wu, Wei & Cui, Bao Tong, 2008. "Global robust exponential stability of delayed neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 35(4), pages 747-754.
    2. Yonggang Chen & Weiping Bi & Yuanyuan Wu, 2008. "Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2008, pages 1-14, November.
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