Passivity criteria for continuous-time neural networks with mixed time-varying delays

被引：49

作者：

Li, Hongyi ^{[1
]}

Lam, James ^{[2
]}

Cheung, K. C. ^{[2
]}

机构：

[1] Harbin Inst Technol, Inst Intelligent Control & Syst, Harbin 150001, Peoples R China

[2] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2012年 / 218卷 / 22期

关键词：

Discrete delays; Distributed delays; Interval delays; Neural networks; Passivity; GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; DISSIPATIVITY ANALYSIS; SINGULAR SYSTEMS; ASYMPTOTIC STABILITY; STATE ESTIMATION; DISCRETE;

D O I：

10.1016/j.amc.2012.05.002

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper is concerned with the problem of passivity analysis for uncertain continuous-time neural networks with mixed time-varying delays. The mixed time-varying delays consist of both discrete and distributed delays, in which the discrete delays are assumed to be varying within a given interval. In addition, the uncertainties are assumed to be norm-bounded. By employing a novel Lyapunov-Krasovskii functional, new passivity delay-interval-dependent criteria are established to guarantee the passivity performance. When estimating an upper bound of the derivative of the Lyapunov-Krasovskii functional, we handle the terms related to the discrete and distributed delays appropriately so as to develop less conservative results. These passivity conditions are presented in terms of linear matrix inequalities, which can be easily solved via standard numerical software. Some numerical examples are given to illustrate the effectiveness of the proposed method. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：11062 / 11074

页数：13

共 49 条

[11] A new augmented Lyapunov-Krasovskii functional approach to exponential passivity for neural networks with time-varying delays [J].

Kwon, O. M. ;

Park, Ju H. ;

Lee, S. M. ;

Cha, E. J. .

APPLIED MATHEMATICS AND COMPUTATION, 2011, 217 (24) :10231-10238

[12]

Lam J., INT J CIRCUIT THEORY

[13] Passivity analysis of neural networks with time delay [J].

Li, CG ;

Liao, XF .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2005, 52 (08) :471-475

[14] Robust Stability for Uncertain Delayed Fuzzy Hopfield Neural Networks With Markovian Jumping Parameters [J].

Li, Hongyi ;

Chen, Bing ;

Zhou, Qi ;

Qian, Weiyi .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2009, 39 (01) :94-102

[15] Stabilization of Markovian jump linear system over networks with random communication delay [J].

Liu, Ming ;

Ho, Daniel W. C. ;

Niu, Yugang .

AUTOMATICA, 2009, 45 (02) :416-421

[16] On global exponential stability of generalized stochastic neural networks with mixed time-delays [J].

Liu, Yurong ;

Wang, Zidong ;

Liu, Xiaohui .

NEUROCOMPUTING, 2006, 70 (1-3) :314-326

[17] Global exponential stability of generalized recurrent neural networks with discrete and distributed delays [J].

Liu, Yurong ;

Wang, Zidong ;

Liu, Xiaohui .

NEURAL NETWORKS, 2006, 19 (05) :667-675

[18] Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays [J].

Liu, Yurong ;

Wang, Zidong ;

Liang, Jinling ;

Liu, Xiaohui .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2009, 20 (07) :1102-1116

[19] A delay-dependent approach to passivity analysis for uncertain neural networks with time-varying delay [J].

Lu, Chien-Yu ;

Tsai, Hsun-Heng ;

Su, Te-Jen ;

Tsai, Jason Sheng-Hong ;

Liao, Chin-Wen .

NEURAL PROCESSING LETTERS, 2008, 27 (03) :237-246

[20] A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay [J].

Mou, Shaoshuai ;

Gao, Huijun ;

Lam, James ;

Qiang, Wenyi .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2008, 19 (03) :532-535

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