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Exponential Stability of Cohen‐Grossberg Neural Networks with Impulse Time Window

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  • Mei Liu
  • Haijun Jiang
  • Cheng Hu

Abstract

This paper concerns the problem of exponential stability for a class of Cohen‐Grossberg neural networks with impulse time window and time‐varying delays. In our letter, the impulsive effects we considered can stochastically occur at a definitive time window and the impulsive controllers we considered can be nonlinear and even rely on the states of all the neurons. Hence, the impulses here can be more applicable and more general. By utilizing Lyapunov functional theory, inequality technique, and the analysis method, we obtain some novel and effective exponential stability criteria for the Cohen‐Grossberg neural networks. These results generalize a few previous known results and numerical simulations are given to show the effectiveness of the derived results.

Suggested Citation

  • Mei Liu & Haijun Jiang & Cheng Hu, 2016. "Exponential Stability of Cohen‐Grossberg Neural Networks with Impulse Time Window," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2016(1).
    • Handle: RePEc:wly:jnddns:v:2016:y:2016:i:1:n:2762960
    DOI: 10.1155/2016/2762960
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    References listed on IDEAS

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    1. Mohamad, Sannay, 2007. "Exponential stability in Hopfield-type neural networks with impulses," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 456-467.
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