Global Exponential Stability of a Class of Variable Time-delay Cellular Neural Networks

被引:1
|
作者
Zhang Changfan [1 ]
Zhang Miaoying [1 ]
Zhang Faming [1 ]
机构
[1] Hunan Univ Technol, Coll Elect & Informat Engn, Wuhan 412008, Hunan, Peoples R China
来源
2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA) | 2014年
关键词
Lyapanav junction; Cellular neural networks; exponential stability;
D O I
10.1109/ISDEA.2014.122
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In view of variable time-delay cellular neural networks with activation function bounded and meeting the conditions of Lippschitz, the result of the global exponential stability is obtained by constructing a special Lyapunov function equality and using Lyapunov function technology and matrix inequality. The global exponential stability of variable time-delay cellular neural networks whose activation function is piecewise linear function is discussed. An example and its computer simulation is given to prove the effectiveness of the obtained result. Finally the result of this paper is discussed by comparing existing achievements, and it is with advantage of low dimension validated matrix, simple calculation and easy computer implementation.
引用
收藏
页码:517 / 519
页数:3
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