Event-based exponential synchronization of complex networks
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- 作者：Bo Zhou ; Xiaofeng Liao ; Tingwen Huang
- 关键词：Event ; triggered synchronization ; Complex networks ; Strongly connected network ; Directed spanning tree ; Convergence rate
- 刊名：Cognitive Neurodynamics
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：10
- 期：5
- 页码：423-436
- 全文大小：1,279 KB
- 刊物主题：Biomedicine general; Neurosciences; Computer Science, general; Artificial Intelligence (incl. Robotics); Biochemistry, general; Cognitive Psychology;
- 出版者：Springer Netherlands
- ISSN：1871-4099
- 卷排序：10
文摘
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.