Exponential Stabilization of Neural Networks With Various Activation Functions and Mixed Time-Varying Delays

被引：94
|
作者：
Phat, V. N. [1 ]
Trinh, H. [2 ]
机构：
[1] Vietnam Acad Sci & Technol, Inst Math, Hanoi 10307, Vietnam
[2] Deakin Univ, Sch Engn, Geelong, Vic 3217, Australia
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2010年 / 21卷 / 07期
关键词：
Linear matrix inequalities; Lyapunov function; mixed delay; neural networks; stabilization; GLOBAL STABILITY-CRITERION; ROBUST STABILITY; DISCRETE; SYSTEMS;
D O I：
10.1109/TNN.2010.2049118
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents some results on the global exponential stabilization for neural networks with various activation functions and time-varying continuously distributed delays. Based on augmented time-varying Lyapunov-Krasovskii functionals, new delay-dependent conditions for the global exponential stabilization are obtained in terms of linear matrix inequalities. A numerical example is given to illustrate the feasibility of our results.
引用
收藏
页码：1180 / 1184
页数：5
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