东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (3): 313-316.DOI: -

• 论著 • 上一篇    下一篇

带有不确定性的时变时滞神经网络渐近稳定性分析

宫大为;冯健;刘金海;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60774093,60774048,60728307);;

Global asymptotic stability for uncertain cellular neural networks with time-varying delays

Gong, Da-Wei (1); Feng, Jian (1); Liu, Jin-Hai (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Published:2013-06-20
  • Contact: Gong, D.-W.
  • About author:-
  • Supported by:
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摘要： 研究了一类既具有时变时滞,又带有不确定性的细胞神经网络全局渐近稳定性问题,并给出了新的稳定判据.其中考虑的不确定性为有界不确定性,系统参数具有的这种不确定性是与时间相关的,它的参数被限制在一定范围内,时间滞后函数是随时间变化而改变的,但它的导数是小于1的.在国际上,针对这一类综合性问题的研究并无太成熟理论.因此,所建模型与以往模型相比更具有一般性.通过构造新的Lyapunov函数,利用LMI方法,给出了系统稳定的充分条件.最后,用仿真实例证明了理论的有效性.

关键词: 神经网络, 时变时滞, 全局渐近稳定, 不确定性, 线性矩阵不等式

Abstract: The global asymptotic stability of cellular neural networks with uncertain parameters and time-varying delays was investigated, thus giving a new criterion for stability, where the relevant uncertainties are bounded and the uncertainties of system parameters are in relation to time hence restricted in a certain range. The time delay function changes with time, and its derivative is less than 1. For such a complicated problem, there is no highly mature theory about it internationally until now. So, the model to be developed relevantly will be more general in comparison with that as shown in earlier works. Framing properly a new Lyapunov funtion and taking LMI (linear matrix inequality), the sufficient conditions are given to system stability. Two simulative examples are given to illustrate theoretically the effectiveness of the method proposed.

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