Identifying Topologies of Complex Dynamical Networks With Stochastic Perturbations
Identifying Topologies of Complex Dynamical Networks With Stochastic Perturbations
复制标题
识别具有随机扰动的复杂动态网络的拓扑
DOI：
10.1109/tcns.2015.2482178
复制
发表时间：
2016-12-01
影响因子：
4.2
通讯作者：
Lu, Jun-an
中科院分区：
文献类型：
--
作者：
Wu, Xiaoqun;Zhao, Xueyi;Lu, Jun-an
网络形式的系统在世界范围内大量存在，引起了多学科非线性科学界的广泛关注。众所周知，网络拓扑在确定网络的内在动力学和功能方面起着重要作用。在过去的十年中，许多研究人员研究了具有给定或已知拓扑结构的复杂网络的几何特征、控制和同步。然而，在许多实际情况下，网络的确切结构通常是未知的。因此，推断复杂网络的内在拓扑结构是理解和解释建立在这些网络上的系统的演化机制和功能行为的先决条件。此外，噪声在自然界和人造网络中无处不在。本文的目标是提出一种简单而有效的技术来恢复噪声污染的复杂动态网络的底层拓扑结构，有或没有信息传输延迟。通过一个由模糊神经网络系统组成的复杂网络，说明了该方法的有效性。此外，还进一步探讨了网络参数对识别性能的影响。
Systems taking the form of networks abound in the world and attract extensive attention from the multidisciplinary nonlinear science community. As is known, network topology plays an important role in determining a network's intrinsic dynamics and function. In the past decade, many researchers have investigated the geometric features, control, and synchronization of complex networks with given or known topological structures. However, in many practical situations, the exact structure of a network is usually unknown. Therefore, inferring the intrinsic topology of complex networks is a prerequisite to understanding and explaining the evolutionary mechanisms and functional behaviors of systems built upon those networks. Furthermore, noise is ubiquitous in nature and in man-made networks. The goal of this paper is to present a simple and efficient technique to recover the underlying topologies of noise-contaminated complex dynamical networks with or without information transmission delay. The effectiveness of the approach is illustrated with a complex network composed of FHN systems. In addition, the impact of some network parameters on identification performance is further probed into.