Finite-Time Lag Synchronization of Delayed Neural Networks via Periodically Intermittent Control

被引：7
|
作者：
Jing, Taiyan [1 ,2 ]
Chen, Fangqi [2 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Dept Mech, Nanjing 210016, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Dept Math, Nanjing 210016, Jiangsu, Peoples R China
来源：
COMPLEXITY | 2016年 / 21卷 / S1期
基金：
新加坡国家研究基金会; 中国国家自然科学基金;
关键词：
complex network; finite-time lag synchronization; control strengths; periodically intermittent control; updated laws; sliding surface; GENERAL COMPLEX NETWORKS; CHAOTIC SYSTEMS; VARYING DELAYS; EXPONENTIAL SYNCHRONIZATION; ADAPTIVE SYNCHRONIZATION; FEEDBACK SYNCHRONIZATION; GLOBAL SYNCHRONIZATION; UNKNOWN-PARAMETERS; DYNAMICAL NETWORKS; STABILITY;
D O I：
10.1002/cplx.21733
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The problem of finite-time lag synchronization of delayed neural networks via periodically intermittent control is studied. In two sections, based on the same finite-time stability theory and using the same sliding mode control, by designing a periodically intermittent feedback controller and adjusting periodically intermittent control strengths with the updated laws, we achieve the finite-time lag synchronization between two time delayed networks. In addition, we ensure that the trajectory of the error system converges to a chosen sliding surface within finite time and keeps it on forever. Finally, two examples are presented to verify the effectiveness of the analytical results obtained here. (C) 2015 Wiley Periodicals, Inc.
引用
收藏
页码：211 / 219
页数：9
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