Zhou Bo, Liao Xiaofeng, Huang Tingwen
College of Electronic and Information Engineering, Southwest University, Chongqing, 400716 China.
Texas A&M University at Qatar, 23874, Doha, Qatar.
Cogn Neurodyn. 2016 Oct;10(5):423-36. doi: 10.1007/s11571-016-9391-3. Epub 2016 Jun 6.
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.
在本文中,我们考虑复杂网络的指数同步。节点之间的信息扩散由适当定义的事件驱动。通过运用M矩阵理论、代数图论和李雅普诺夫方法,设计了两种分布式事件触发律,避免了节点之间的连续通信。然后,给出了确保基于事件的指数同步的几个准则,并获得了指数收敛速率。此外,我们证明了在同步实现之前可以排除事件触发律的芝诺行为,即事件间时间的下限严格为正。最后,提供了一个仿真例子来说明理论分析的有效性。