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Global exponential periodicity of a class of neural networks with recent-history distributed delays

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  • Yang, Xiaofan
  • Liao, Xiaofeng
  • Megson, Graham M.
  • Evans, David J.

Abstract

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system.

Suggested Citation

  • Yang, Xiaofan & Liao, Xiaofeng & Megson, Graham M. & Evans, David J., 2005. "Global exponential periodicity of a class of neural networks with recent-history distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 441-447.
    • Handle: RePEc:eee:chsofr:v:25:y:2005:i:2:p:441-447
    DOI: 10.1016/j.chaos.2004.11.014
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    References listed on IDEAS

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    1. Sun, Changyin & Feng, Chun-Bo, 2004. "Exponential periodicity and stability of delayed neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 66(6), pages 469-478.
    2. Mohamad, S. & Gopalsamy, K., 2000. "Dynamics of a class of discrete-time neural networks and their continuous-time counterparts," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 53(1), pages 1-39.
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    Cited by:

    1. Jiang, Haijun & Teng, Zhidong, 2006. "Boundedness and global stability for nonautonomous recurrent neural networks with distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 83-93.

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