Output Discernibility of Topological Variations in Linear Dynamical Networks

被引：12

作者：

Fan, Ziye ^{[1
]}

Wu, Xiaoqun ^{[1
,2
]}

Mao, Bing ^{[1
]}

Lu, Jinhu ^{[3
,4
]}

机构：

[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China

[2] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China

[3] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[4] Zhongguancun Lab, Beijing 100191, Peoples R China

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2024年 / 69卷 / 08期

基金：

国家自然科学基金重大研究计划; 中国国家自然科学基金;

关键词：

Topology; Network topology; Sufficient conditions; Eigenvalues and eigenfunctions; Trajectory; Artificial neural networks; Time series analysis; Dynamical networks; output discernibility; topological variations;

D O I：

10.1109/TAC.2024.3366315

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The conditions under which topological variations in networked linear dynamical systems can be discerned from their outputs are investigated. The output-indiscernible space is completely characterized without conditions imposed on the topology matrix. It is demonstrated that a topological change can be output-indiscernible even if the original and the altered topology matrices share no common eigenvalues. Furthermore, the necessary and sufficient condition for output discernibility is proposed, which is based on the observation matrix and the Jordan chains of the topology matrices. In addition, a necessary condition distinct from discernibility conditions and two sufficient conditions for easy verification are derived. Examples on consensus dynamics are provided, highlighting the potential of our results in guiding sensor node allocation and initial value selection for detecting topological changes.

引用

页码：5524 / 5530

页数：7

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