一种改进的复杂动态网络拓扑识别方法

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yi Zheng;Xiaoqun Wu;Ziye Fan;Kebin Chen;Jinhu Lü
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引用次数: 0

摘要

在过去的十年中,已经提出了许多基于同步的识别方法来解决识别未知网络拓扑的挑战。线性无关条件(LIC)是这些方法的基本要求,但该条件存在一些问题。在本文中,我们提出了一种改进的基于无lic同步的识别方法来解决上述问题。具体而言,构建由满足特定条件的孤立节点组成的驱动网络,将拓扑结构未知的网络定义为响应网络。通过设计合适的控制器和更新规律,驱动网络和响应网络实现同步,同时估计矩阵准确识别未知拓扑矩阵。我们的方法被证明是现有的无lic识别方法的一种推广形式。此外,我们引入了一个新的证明框架,从理论上证明了我们方法的有效性。最后,通过两个仿真实例验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Topology Identification Method of Complex Dynamical Networks
Over the past decade, numerous synchronization-based identification methods have been proposed to address the challenge of identifying unknown network topologies. The linear independence condition (LIC) is an essential requirement in these methods, however, there are issues with this condition. In this article, we propose an improved LIC-free synchronization-based identification method to address above issues. Specifically, a drive network consisting of isolated nodes that satisfy specific conditions is constructed, and the network containing an unknown topology is defined as the response network. Through the design of appropriate controllers and update laws, the drive network and the response network achieve synchronization, while the estimation matrix accurately identifies the unknown topology matrix. Our method is proven to be a generalized form of the existing LIC-free identification methods. Furthermore, we introduce a novel proof framework to theoretically demonstrate the effectiveness of our method. Finally, two simulation examples demonstrate the effectiveness of the proposed method.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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