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New delay-dependent global robust stability conditions for interval neural networks with time-varying delays

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  • Zong, Guangdeng
  • Liu, Jia

Abstract

This paper deals with the problem of delay-dependent global robust stability analysis for interval neural networks with time-varying delays. By introducing an equivalent transformation of interval systems and the free-weighting matrix technique, a new delay-dependent condition on global robust stability is established. This condition is presented in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. A numerical example is provided to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Zong, Guangdeng & Liu, Jia, 2009. "New delay-dependent global robust stability conditions for interval neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 2954-2964.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:5:p:2954-2964
    DOI: 10.1016/j.chaos.2009.04.038
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    References listed on IDEAS

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    1. 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.
    2. Liu, Hailin & Chen, Guohua, 2007. "Delay-dependent stability for neural networks with time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(1), pages 171-177.
    3. Cao, Jinde & Ho, Daniel W.C., 2005. "A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 24(5), pages 1317-1329.
    4. Arik, Sabri, 2005. "Global robust stability analysis of neural networks with discrete time delays," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1407-1414.
    5. Yang, Haifeng & Chu, Tianguang & Zhang, Cishen, 2006. "Exponential stability of neural networks with variable delays via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 133-139.
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