Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties

被引：59

作者：

Zeng, Hong-Bing ^{[1
,2
]}

Park, Ju H. ^{[2
]}

Xia, Jian-Wei ^{[3
]}

机构：

[1] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412007, Peoples R China

[2] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea

[3] Liaocheng Univ, Sch Math Sci, Liaocheng 252000, Shandong, Peoples R China

来源：

NONLINEAR DYNAMICS | 2015年 / 79卷 / 01期

基金：

新加坡国家研究基金会; 中国国家自然科学基金;

关键词：

Neural networks; Time delay; Randomly occurring uncertainties; Dissipativity; GLOBAL ASYMPTOTIC STABILITY; H-INFINITY CONTROL; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; DISCRETE; SYSTEMS; PARAMETERS; CRITERIA; MATRIX;

D O I：

10.1007/s11071-014-1646-0

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In this paper, the problem of robust dissipativity is investigated for neural networks with both time-varying delay and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to obey mutually uncorrelated Bernoulli-distributed white noise sequences. By constructing a new Lyapunov-Krasovskii functional, some improved delay-dependent dissipativity conditions are derived based on two integral inequalities, which are formulated in terms of linear matrix inequality. Furthermore, some information of activation function ignored in previous works has been taken into account in the resulting condition. The effectiveness and the improvement of the proposed approach are demonstrated by two illustrating numerical examples.

引用

页码：83 / 91

页数：9

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