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New Results for Global Exponential Stability of Delayed Cohen-Grossberg Neural Networks

  • Anhua Wan
  • Hong Qiao
  • Bo Zhang
  • Weihua Mao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

The exponential stability is discussed for Cohen-Grossberg neural networks with discrete delays. Without assuming the boundedness, differentiability and monotonicity of the activation functions, the nonlinear measure approach is employed to analyze the existence and uniqueness of an equilibrium, and a novel Lyapunov functional is constructed to investigate the exponential stability of the networks. New general sufficient conditions, which are independent of the delays, are derived for the global exponential stability of the delayed neural networks.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anhua Wan
    • 1
  • Hong Qiao
    • 1
  • Bo Zhang
    • 2
  • Weihua Mao
    • 3
    • 4
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Institute of Applied MathematicsChinese Academy of SciencesBeijingChina
  3. 3.College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina
  4. 4.College of ScienceSouth China Agricultural UniversityGuangzhouChina

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