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Stability analysis of almost periodic solutions for delayed neural networks without global Lipschitz activation functions

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  • Zhou, Jun
  • Zhao, Weirui
  • Lv, Xiaohong
  • Zhu, Huaping

Abstract

In this paper, the existence and local exponential stability of the almost periodic solutions for recurrent neural networks with mixed delays have been investigated. By applying Dini derivative and introducing many real parameters, and estimating the upper bound of solutions of the system, a series of new and useful criteria on the existence and local exponential stability of almost periodic for general delayed neural networks without global Lipschitz activation functions have been derived. Those results obtained in this paper extend and generalize the corresponding results existing in the previous literature. Two examples and numerical simulations are given to illustrate our theory.

Suggested Citation

  • Zhou, Jun & Zhao, Weirui & Lv, Xiaohong & Zhu, Huaping, 2011. "Stability analysis of almost periodic solutions for delayed neural networks without global Lipschitz activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(11), pages 2440-2455.
    • Handle: RePEc:eee:matcom:v:81:y:2011:i:11:p:2440-2455
    DOI: 10.1016/j.matcom.2011.03.009
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Tiejun & Wang, Min & Li, Chen, 2015. "Almost periodic solution for multidirectional associative memory neural network with distributed delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 107(C), pages 52-60.

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