东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2006, Vol. 27 ›› Issue (4): 367-370.DOI: -

• 论著 • 上一篇    下一篇

多时变时滞细胞神经网络的全局指数稳定性

王占山;张化光;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-23 修回日期:2013-06-23 出版日期:2006-04-15 发布日期:2013-06-23
  • 通讯作者: Wang, Z.-S.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(6024401760325311);;

Globally exponential stability of cellular neural networks with multiple time-varying delays

Wang, Zhan-Shan (1); Zhang, Hua-Guang (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-23 Revised:2013-06-23 Online:2006-04-15 Published:2013-06-23
  • Contact: Wang, Z.-S.
  • About author:-
  • Supported by:
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摘要： 针对一类多时变时滞细胞神经网络,利用Young不等式和Halanay不等式技术,给出了保证平衡点惟一性和全局指数稳定性的几个充分判据.所得到的全局指数稳定判据完全独立于时滞,不要求时变时滞的可微性和神经元激励函数的严格单调性,且通过几个注释说明本文的结果改进和扩展了现有一些文献中的结果.仿真例子证明了本文结果的有效性.

关键词: 全局指数稳定, 细胞神经网络, 多时变时滞, Lyapunov函数, Young不等式

Abstract: Globally exponential stability of a class of cellular neural networks with multiple time varying delays is investigated. Using the technique by virtue of Young and Halanay inequalities, some new sufficient criteria are given to ensure the uniqueness of equilibrium point and globally exponential stability. In this way the criteria given for globally exponential stability are entirely independent of time delay without the differentiability of time-varying delay and the strict monotonicity of neuron's excitation function. In addition, some remarks are given to explain how the results as shown in this paper improves and extends the earlier works as references of which the effectiveness is proved via simulation example.

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