Stability analysis of switched cellular neural networks: A mode-dependent average dwell time approach

被引：63
作者：
Huang, Chuangxia [1 ]
Cao, Jie [1 ]
Cao, Jinde [2 ,3 ]
机构：
[1] Changsha Univ Sci & Technol, Coll Math & Comp Sci, Changsha 410114, Hunan, Peoples R China
[2] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[3] King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia
来源：
NEURAL NETWORKS | 2016年 / 82卷
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Switched system; Cellular neural network; Stability; Mode-dependent average dwell time; Lyapunov method; GLOBAL ASYMPTOTIC STABILITY; VARYING DELAYS; EXPONENTIAL STABILITY; UNSTABLE SUBSYSTEMS; LINEAR-SYSTEMS; DISCRETE; PERIODICITY; PASSIVITY; CRITERIA; SYNCHRONIZATION;
D O I：
10.1016/j.neunet.2016.07.009
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 addresses the exponential stability of switched cellular neural networks by using the mode dependent average dwell time (MDADT) approach. This method is quite different from the traditional average dwell time (ADT) method in permitting each subsystem to have its own average dwell time. Detailed investigations have been carried out for two cases. One is that all subsystems are stable and the other is that stable subsystems coexist with unstable subsystems. By employing Lyapunov functionals, linear matrix inequalities (LMIs), Jessen-type inequality, Wirtinger-based inequality, reciprocally convex approach, we derived some novel and less conservative conditions on exponential stability of the networks. Comparing to ADT, the proposed MDADT show that the minimal dwell time of each subsystem is smaller and the switched system stabilizes faster. The obtained results extend and improve some existing ones. Moreover, the validness and effectiveness of these results are demonstrated through numerical simulations. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：84 / 99
页数：16
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