Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks

被引：94
作者：
Tu, Zhengwen [1 ,2 ,3 ,4 ]
Cao, Jinde [1 ,2 ,5 ]
Hayat, Tasawar [5 ,6 ]
机构：
[1] Southeast Univ, Dept Math, Nanjing 210996, Jiangsu, Peoples R China
[2] Southeast Univ, Res Ctr Complex Syst & Network Sci, Nanjing 210996, Jiangsu, Peoples R China
[3] Chongqing Three Gorges Univ, Sch Math & Stat, Chongqing 404100, Peoples R China
[4] Chongqing Three Gorges Univ, Key Lab Nonlinear Sci & Syst Struct, Chongqing 404100, Peoples R China
[5] King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia
[6] Quaid I Azam Univ, Dept Math, Islamabad 44000, Pakistan
来源：
NEURAL NETWORKS | 2016年 / 75卷
基金：
中国国家自然科学基金;
关键词：
Inertial neural networks; Dissipativity; Uncertainty; Matrix measure; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; DISCONTINUOUS ACTIVATIONS; DISTRIBUTED DELAYS; HAIR-CELLS; SYNCHRONIZATION; BIFURCATION; CRITERION; SYSTEMS;
D O I：
10.1016/j.neunet.2015.12.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The present paper is devoted to investigating the global dissipativity for inertial neural networks with time-varying delays and parameter uncertainties. By virtue of a suitable substitution, the original system is transformed to the first order differential system. By means of matrix measure, generalized Halanay inequality, and matrix-norm inequality, several sufficient criteria for the global dissipativity of the addressed neural networks are proposed. Meanwhile, the specific estimations of positive invariant sets and globally attractive sets are obtained. Finally, two examples are provided to validate our theoretical results. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：47 / 55
页数：9
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