Event-based exponential synchronization of complex networks.

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• Cogn Neurodyn

altmetric score

• 0.75

author list (cited authors)

• Zhou, B. o., Liao, X., & Huang, T.

citation count

• 5

complete list of authors

• Zhou, Bo||Liao, Xiaofeng||Huang, Tingwen

publication date

• October 2016

publisher

• Springer Nature  Publisher

published in

• Cognitive Neurodynamics  Journal

keywords

• Complex Networks
• Convergence Rate
• Directed Spanning Tree
• Event-triggered Synchronization
• Strongly Connected Network

PubMed Central ID

• 27668021

Digital Object Identifier (DOI)

• 10.1007/s11571-016-9391-3

start page

• 423

end page

• 436

volume

• 10

issue

• 5

URL

• http%3A%2F%2Fdx.doi.org%2F10.1007%2Fs11571-016-9391-3