Robust passivity analysis for neutral-type neural networks with mixed and leakage delays

被引：22

作者：

Samidurai, R. ^{[1
]}

Rajavel, S. ^{[1
]}

Zhu, Quanxin ^{[2
,3
]}

Raja, R. ^{[4
]}

Zhou, Hongwei ^{[5
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

[2] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China

[3] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China

[4] Alagappa Univ, Ramanujan Ctr Higher Math, Karaikkudi 630004, Tamil Nadu, India

[5] Nanjing Xiaozhuang Univ, Sch Informat Engn, Nanjing 211171, Jiangsu, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 175卷

基金：

中国国家自然科学基金;

关键词：

Neutral-type neural network; Passivity; Lyapunov-Krasovskii functional; Leakage delay; Convex combination technique; GLOBAL EXPONENTIAL STABILITY; TIME-DELAY; DISCRETE;

D O I：

10.1016/j.neucom.2015.10.103

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 investigates the problem of passivity of neutral-type neural networks with mixed and leakage delays. By establishing a suitable augmented Lyapunov functional and combining a new integral inequality with the reciprocally convex combination technique, we obtain some sufficient passivity conditions, which are formulated in terms of linear matrix inequalities (LMIs). Here, some useful information on the neuron activation function ignored in the existing literature is taken into account Finally, some numerical examples are given to demonstrate the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：635 / 643

页数：9

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