Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism

被引：130
作者：
Li, Lulu [1 ]
Ho, Daniel W. C. [2 ]
Cao, Jinde [3 ]
Lu, Jianquan [3 ]
机构：
[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
[2] City Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
[3] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
来源：
NEURAL NETWORKS | 2016年 / 76卷
基金：
中国国家自然科学基金;
关键词：
Neural networks; Cluster synchronization; Pinning control; Event-based mechanism; COMPLEX NETWORKS; EXPONENTIAL STABILIZATION; DYNAMICAL NETWORKS; STABILITY; CONTROLLABILITY; STRATEGY; DELAYS;
D O I：
10.1016/j.neunet.2015.12.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Cluster synchronization is a typical collective behavior in coupled dynamical systems, where the synchronization occurs within one group, while there is no synchronization among different groups. In this paper, under event-based mechanism, pinning cluster synchronization in an array of coupled neural networks is studied. A new event-triggered sampled-data transmission strategy, where only local and event-triggering states are utilized to update the broadcasting state of each agent, is proposed to realize cluster synchronization of the coupled neural networks. Furthermore, a self-triggered pinning cluster synchronization algorithm is proposed, and a set of iterative procedures is given to compute the event-triggered time instants. Hence, this will reduce the computational load significantly. Finally, an example is given to demonstrate the effectiveness of the theoretical results. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：1 / 12
页数：12
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