New Results for Global Stability of Cohen-Grossberg Neural Networks with Discrete Time Delays

  • Zeynep Orman
  • Sabri Arik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


This paper studies the global convergence properties of Cohen-Grossberg neural networks with discrete time delays. Without assuming the symmetry of interconnection weight coefficients, and the monotonicity and differentiability of activation functions, and by employing Lyapunov functionals, we derive new delay independent sufficient conditions under which a delayed Cohen-Grossberg neural network converges to a globally asymptotically stable equilibrium point. Some examples are given to illustrate the advantages of the results over the previously reported results in the literature.


Equilibrium Point Exponential Stability Global Stability Cellular Neural Network Global Asymptotic Stability 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zeynep Orman
    • 1
  • Sabri Arik
    • 1
  1. 1.Department of Computer Engineering, Faculty of EngineeringIstanbul UniversityAvcilarTurkey

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