Robust passivity analysis for uncertain neural networks with discrete and distributed time-varying delays

被引：24
|
作者：
Ge, Chao [1 ]
Park, Ju H. [2 ]
Hua, Changchun [3 ]
Shi, Caijuan [1 ]
机构：
[1] North China Univ Sci & Technol, Inst Informat Engn, Tangshan, Peoples R China
[2] Yeungnam Univ, Dept Elect Engn, Kyongsan, South Korea
[3] Yanshan Univ, Inst Elect Engn, Qinhuangdao, Hebei, Peoples R China
来源：
NEUROCOMPUTING | 2019年 / 364卷
基金：
新加坡国家研究基金会;
关键词：
Uncertain neural networks; Passivity analysis; Time-varying delays; Lyapunov-Krasovskii functional; SAMPLED-DATA SYSTEMS; CHAOTIC LURE SYSTEMS; DECOMPOSING APPROACH; STABILITY; SYNCHRONIZATION; CRITERIA;
D O I：
10.1016/j.neucom.2019.06.077
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we address robust passivity analysis for a class of uncertain neural networks (NNs) with discrete and distributed time-varying delays. By selecting an improved Lyapunov-Krasovskii functional (LKF) with a novel delay-produce-type (DPT) term and combing free-matrix-based (FMB) integral inequality, some sufficient criteria are obtained to guarantee the passivity of uncertain NNs. Then, the maximal allowable upper bound (MAUB) of time-varying delay can be obtained by reciprocally convex combination (RCC) technique through solving a group of linear matrix inequalities (LMIs). Finally, numerical examples are considered to illustrate the benefit and superiority of the method proposed. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：330 / 337
页数：8
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