Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time-Varying Delay

被引:0
|
作者
Luo, Wenguang [1 ,2 ]
Wang, Xiuling [1 ]
Liu, Yonghua [1 ]
Lan, Hongli [3 ]
机构
[1] Guangxi Univ Sci & Technol, Sch Elect & Informat Engn, Liuzhou 545006, Peoples R China
[2] Guangxi Univ Sci & Technol, Guangxi Key Lab Automobile Components & Vehicle T, Liuzhou 545006, Peoples R China
[3] Guangxi Univ Sci & Technol, Sch Comp Engn, Liuzhou 545006, Peoples R China
关键词
ASYMPTOTIC STABILITY; PROGRAMMING PROBLEMS; ASSOCIATIVE MEMORY; LMI APPROACH; MODEL;
D O I
10.1155/2013/540951
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The problem of global exponential stability for recurrent neural networks with time-varying delay is investigated. By dividing the time delay interval [0, tau(t)] into K + 1 dynamical subintervals, a new Lyapunov-Krasovskii functional is introduced; then, a novel linear-matrix-inequality (LMI-) based delay-dependent exponential stability criterion is derived, which is less conservative than some previous literatures (Zhang et al., 2005; He et al., 2006; and Wu et al., 2008). An illustrate example is finally provided to show the effectiveness and the advantage of the proposed result.
引用
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页数:7
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