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Harmless delays for global exponential stability of Cohen–Grossberg neural networks

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  • Zhao, Weirui
  • Tan, Yong

Abstract

In this paper, the Cohen–Grossberg neural networks with time delays are considered without assuming any symmetry of connection matrix and differentiability of the activation functions. By constructing a novel Lyapunov functional, new sufficient criteria for the existence of a unique equilibrium and global exponential stability of the network are derived. These criteria are all independent of the magnitudes of delays, and so the delays under these conditions are harmless. Those results are shown to generalize the previous global exponential stability results derived in the literature.

Suggested Citation

  • Zhao, Weirui & Tan, Yong, 2007. "Harmless delays for global exponential stability of Cohen–Grossberg neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 74(1), pages 47-57.
    • Handle: RePEc:eee:matcom:v:74:y:2007:i:1:p:47-57
    DOI: 10.1016/j.matcom.2006.08.003
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    References listed on IDEAS

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    1. 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.
    2. Tu, Fenghua & Liao, Xiaofeng, 2005. "Harmless delays for global asymptotic stability of Cohen–Grossberg neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 26(3), pages 927-933.
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    Cited by:

    1. Wang, Xiaohu & Guo, Qingyi & Xu, Daoyi, 2009. "Exponential p-stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1698-1710.

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