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Almost periodic solutions for Hopfield neural networks with continuously distributed delays

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  • Liu, Bingwen

Abstract

In this paper Hopfield neural networks with continuously distributed delays are considered. Without assuming the global Lipschitz conditions of activation functions, sufficient conditions for the existence and exponential stability of the almost periodic solutions are established by using the fixed point theorem and differential inequality techniques. The results of this paper are new and they complement previously known results.

Suggested Citation

  • Liu, Bingwen, 2007. "Almost periodic solutions for Hopfield neural networks with continuously distributed delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(5), pages 327-335.
    • Handle: RePEc:eee:matcom:v:73:y:2007:i:5:p:327-335
    DOI: 10.1016/j.matcom.2006.05.027
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    References listed on IDEAS

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    1. Liu, Jiang, 2005. "Global exponential stability of Cohen–Grossberg neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 26(3), pages 935-945.
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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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