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

页码：1180 / 1184

页数：5

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