Novel Robust Stability Criteria for Stochastic Hopfield Neural Networks With Time Delays

被引：154

作者：

Yang, Rongni ^{[1
]}

Gao, Huijun

Shi, Peng ^{[2
,3
,4
]}

机构：

[1] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Dept Control Sci & Engn, Harbin 150001, Peoples R China

[2] Univ Glamorgan, Fac Adv Technol, Pontypridd CF37 1DL, M Glam, Wales

[3] Victoria Univ, Inst Logist & Supply Chain Management, Sch Sci & Engn, Melbourne, Vic 8001, Australia

[4] Univ S Australia, Sch Math & Stat, Mawson Lakes, SA 5095, Australia

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2009年 / 39卷 / 02期

基金：

高等学校博士学科点专项科研基金; 英国工程与自然科学研究理事会;

关键词：

Hopfield neural networks (HNNs); Lyapunov-Krasovskii functional; robust stability; stochastic systems; time delay; uncertainties; GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; DEPENDENT STABILITY; DISCRETE; SYSTEMS; STATE;

D O I：

10.1109/TSMCB.2008.2006860

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the problem of asymptotic stability for stochastic Hopfield neural networks (HNNs) with time delays is investigated. New delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional. Moreover, the results are further extended to the delayed stochastic HNNs with parameter uncertainties. The main idea is based on the delay partitioning technique, which differs greatly from most existing results and reduces conservatism. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.

引用

页码：467 / 474

页数：8

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