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增强扩散耦合动力网络的同步能力:综述。

Enhancing synchronizability of diffusively coupled dynamical networks: a survey.

出版信息

IEEE Trans Neural Netw Learn Syst. 2013 Jul;24(7):1009-22. doi: 10.1109/TNNLS.2013.2250998.

Abstract

In this paper, we review the literature on enhancing synchronizability of diffusively coupled dynamical networks with identical nodes. The last decade has witnessed intensive investigations on the collective behavior over complex networks and synchronization of dynamical systems is the most common form of collective behavior. For many applications, it is desired that the synchronizability-the ability of networks in synchronizing activity of their individual dynamical units-is enhanced. There are a number of methods for improving the synchronization properties of dynamical networks through structural perturbation. In this paper, we survey such methods including adding/removing nodes and/or edges, rewiring the links, and graph weighting. These methods often try to enhance the synchronizability through minimizing the eigenratio of the Laplacian matrix of the connection graph-a synchronizability measure based on the master-stability-function formalism. We also assess the performance of the methods by numerical simulations on a number of real-world networks as well as those generated through models such as preferential attachment, Watts-Strogatz, and Erdos-Rényi.

摘要

在本文中,我们回顾了关于增强具有相同节点的弥散耦合动力网络同步性的文献。在过去的十年中,人们对复杂网络上的集体行为进行了深入研究,而动力学系统的同步是最常见的集体行为形式。对于许多应用,希望增强网络的同步能力——即网络使各个动力学单元的活动同步的能力。有许多通过结构扰动来改善动力网络同步特性的方法。在本文中,我们调查了包括添加/删除节点和/或边、重新布线链路和图加权在内的这些方法。这些方法通常试图通过最小化连接图拉普拉斯矩阵的特征比来增强同步能力——这是一种基于主稳定性函数形式主义的同步能力度量。我们还通过对一些现实网络以及通过优先连接、Watts-Strogatz 和 Erdos-Rényi 等模型生成的网络进行数值模拟,评估了这些方法的性能。

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