New Criteria of Global Exponential Stability for a Class of Generalized Neural Networks with Time-Varying Delays

  • Gang Wang
  • Hua-Guang Zhang
  • Chong-Hui Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, we essentially drop the requirement of Lipschitz condition on the activation functions. By employing Lyapunov functional and several new inequalities, some new criteria concerning global exponential stability for a class of generalized neural networks with time-varying delays are obtained, which only depend on physical parameters of neural networks. Since these new criteria do not require the activation functions to be differentiable, bounded or monotone nondecreasing and the connection weight matrices to be symmetric, they are mild and more general than previously known criteria.


Neural Network Equilibrium Point Activation Function Lipschitz Condition Global Asymptotic Stability 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gang Wang
    • 1
  • Hua-Guang Zhang
    • 1
  • Chong-Hui Song
    • 1
  1. 1.School of Information Science and EngineeringNortheastern UniversityShenyangPeople’s Republic of China

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