New passivity criteria for uncertain neural networks with time-varying delay

被引：26

作者：

Li, Yuanyuan ^{[1
]}

Zhong, Shouming ^{[1
]}

Cheng, Jun ^{[2
]}

Shi, Kaibo ^{[3
]}

Ren, Jiaojiao ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China

[2] Hubei Univ Nationalities, Sch Sci, Enshi 445000, Hubei, Peoples R China

[3] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 171卷

基金：

中国国家自然科学基金;

关键词：

Neural networks; Uncertainty; Passivity; Time-varying delay; STABILITY-CRITERIA; ROBUST STABILITY; DISCRETE; SYSTEMS; DESIGN; SYNCHRONIZATION; STABILIZATION; CONTROLLERS;

D O I：

10.1016/j.neucom.2015.07.033

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 deals with the problem of passivity analysis for neural network with both time-varying delays and norm-bounded parameter uncertainties. A remarkable approach is proposed for constructing a novel Lyapunov-Krasovskii function involving triple integral terms. It does not requiring all the symmetric matrices to be positive definite. Due to the triple-integral terms and relaxation on the positive-definiteness of every Lyapunov-matrix, the conservatism of the results can be successfully reduced. Finally, numerical examples are given to demonstrate the effectiveness of proposed techniques. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：1003 / 1012

页数：10

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