Impulsive effects on stability of Cohen-Grossberg neural networks with variable delays

  • Authors:

  • Zhichun Yang;Daoyi Xu

  • Affiliations:

  • Institute of Mathematics, Sichuan University, Chengdu, PR China and Basic Department, Chengdu Textile Institute, Chengdu, China;Institute of Mathematics, Sichuan University, Chengdu, PR China and Basic Department, Chengdu Textile Institute, Chengdu, China

  • Venue:
  • Applied Mathematics and Computation
  • Year:
  • 2006

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Abstract

In this paper, a model of impulsive Cohen--Grossberg neural networks is first formulated. By establishing some impulsive differential inequalities, we investigate impulsive effects on the stability of Cohen-Grossberg neural networks with variable delays and obtain some sufficient conditions ensuring global exponential stability of the impulsive delay system. Our criteria not only show that the stability still remains under certain impulsive perturbations for some continuous stable neural networks, but also present an approach to stabilize the unstable neural networks by utilizing impulsive effects. The results extend and improve some recent works for impulsive neural networks as well as non-impulsive neural networks. Some examples and their simulations are given for illustration of the theoretical results.