Event-based exponential synchronization of complex networks

被引：7
|
作者：
Zhou, Bo [1 ]
Liao, Xiaofeng [1 ]
Huang, Tingwen [2 ]
机构：
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400716, Peoples R China
[2] Texas A&M Univ Qatar, Doha 23874, Qatar
来源：
COGNITIVE NEURODYNAMICS | 2016年 / 10卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Event-triggered synchronization; Complex networks; Strongly connected network; Directed spanning tree; Convergence rate; MULTIAGENT SYSTEMS; NEURAL-NETWORKS; TRIGGERED CONTROL; ADAPTIVE SYNCHRONIZATION; PINNING CONTROL; CONSENSUS; COORDINATION; OSCILLATORS; STABILITY; ALGORITHM;
D O I：
10.1007/s11571-016-9391-3
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
In this paper, we consider exponential synchronization of complex networks. The information diffusions between nodes are driven by properly defined events. By employing the M-matrix theory, algebraic graph theory and the Lyapunov method, two kinds of distributed event-triggering laws are designed, which avoid continuous communications between nodes. Then, several criteria that ensure the event-based exponential synchronization are presented, and the exponential convergence rates are obtained as well. Furthermore, we prove that Zeno behavior of the event-triggering laws can be excluded before synchronization being achieved, that is, the lower bounds of inter-event times are strictly positive. Finally, a simulation example is provided to illustrate the effectiveness of theoretical analysis.
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页码：423 / 436
页数：14
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