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Global Exponential Stability of Reaction-Diffusion Neural Networks with Both Variable Time Delays and Unbounded Delay

  • Weifan Zheng
  • Jiye Zhang
  • Weihua Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In the paper, the reaction-diffusion neural network models with both variable time delays and unbounded delay are investigated. These models contain weaker activation functions than partially or globally Lipschitz continuous functions. Without assuming the boundedness, monotonicity and differentiability of the active functions, algebraic criteria ensuring existence, uniqueness and global exponential stability of the equilibrium point are obtained.

Keywords

Neural Network Equilibrium Point Cellular Neural Network Lipschitz Continuous Function 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

  • Weifan Zheng
    • 1
  • Jiye Zhang
    • 1
  • Weihua Zhang
    • 1
  1. 1.National Traction Power LaboratorySouthwest Jiaotong UniversityChengduPeople’s Republic of China

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