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Global asymptotic stability analysis for cellular neural networks with time delays

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  • Lu, Junwei
  • Guo, Yiqian
  • Xu, Shengyuan

Abstract

This paper provides a new sufficient condition for the global asymptotic stability and uniqueness of the equilibrium point of cellular neural networks with time delays. This condition is expressed in terms of linear matrix inequalities, which can be easily checked by various recently developed algorithms in solving convex optimization problems. Numerical examples are provided to show that the proposed stability result is less conservative than some previously established ones in the literature.

Suggested Citation

  • Lu, Junwei & Guo, Yiqian & Xu, Shengyuan, 2006. "Global asymptotic stability analysis for cellular neural networks with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 349-353.
    • Handle: RePEc:eee:chsofr:v:29:y:2006:i:2:p:349-353
    DOI: 10.1016/j.chaos.2005.08.046
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    References listed on IDEAS

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    1. Cheng, Chao-Jung & Liao, Teh-Lu & Hwang, Chi-Chuan, 2005. "Exponential synchronization of a class of chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 197-206.
    2. He, Yong & Wang, Qing-Guo & Zheng, Wei-Xing, 2005. "Global robust stability for delayed neural networks with polytopic type uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1349-1354.
    3. Zhang, Qiang & Wei, Xiaopeng & Xu, Jin, 2005. "Delay-dependent exponential stability of cellular neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 23(4), pages 1363-1369.
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