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Global Asymptotical Stability of Cohen-Grossberg Neural Networks with Time-Varying and Distributed Delays

  • Tianping Chen
  • Wenlian Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this paper, we discuss delayed Cohen-Grossberg neural networks with time-varying and distributed delays and investigate their global asymptotical stability of the equilibrium point. The model proposed in this paper is universal. A set of sufficient conditions ensuring global convergence and globally exponential convergence for the Cohen-Grossberg neural networks with time-varying and distributed delays are given. Most of the existing models and global stability results for Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks can be obtained from the theorems given in this paper.

Keywords

Neural Network Exponential Stability Cellular Neural Network Exponential Convergence Hopfield Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tianping Chen
    • 1
  • Wenlian Lu
    • 1
    • 2
  1. 1.Key Laboratory of Nonlinear Science of Chinese Ministry of Education, Institute of MathematicsFudan UniversityShanghaiP.R. China
  2. 2.Max Planck Institute for Mathematics in the SciencesLeipzigGermany

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