Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays

被引：35
|
作者：
Saravanakumar, R. [1 ,4 ,5 ]
Rajchakit, Grienggrai [1 ]
Ali, M. Syed [2 ]
Xiang, Zhengrong [3 ]
Joo, Young Hoon [4 ]
机构：
[1] Maejo Univ, Dept Math, Fac Sci, Chiang Mai 50290, Thailand
[2] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India
[3] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China
[4] Kunsan Natl Univ, Dept Control & Robot Engn, Kunsan 573701, Chonbuk, South Korea
[5] King Mongkuts Univ Technol Thonburi, Dept Math, Fac Sci, Bangkok 10140, Thailand
来源：
NEURAL COMPUTING & APPLICATIONS | 2018年 / 30卷 / 12期
基金：
新加坡国家研究基金会;
关键词：
Extended dissipativity analysis; Uncertain discrete-time neural networks; Lyapunov method; Linear matrix inequality; EXPONENTIAL STABILITY; PASSIVITY;
D O I：
10.1007/s00521-017-2974-z
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 draft, we consider the problem of robust extended dissipativity for uncertain discrete-time neural networks (DNNs) with time-varying delays. By constructing appropriate Lyapunov-Krasovskii functional (LKF), sufficient conditions are established to ensure that the considered time-delayed uncertain DNN is extended dissipative. The derived conditions are presented in terms of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the superiority of this result.
引用
收藏
页码：3893 / 3904
页数：12
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