LIC-free topology identification of complex dynamic networks

Abstract Over the past decade, synchronization-based identification methods have become a commonly used approach for identifying unknown network topologies. Most existing identification methods rely on the linear independence condition (LIC), which has been shown to have certain limitations. Recently, several new methods have been proposed to overcome this constraint. Building on existing methods, this paper proposes a series of novel predefined signals as drive network. Based on the proposed predefined signals, we develop a novel topology identification method and establish a new proof framework to theoretically validate the effectiveness of our method. Through the designed controllers and update laws, synchronization between the drive and auxiliary networks is achieved, thereby enabling the estimated matrix to accurately identify the unknown topology matrix. Numerical simulations demonstrate the effectiveness of the proposed method and validate the impact of strength selection for the predefined signals on identification performance.

Authors

Institutions

Publication Details

Journal
Europhysics Letters (EPL)
Published
2025-10-13
DOI
https://doi.org/10.1209/0295-5075/ae1252
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LIC-free topology identification of complex dynamic networks

Xiaoqun Wu, Yi Zheng, Hao Liao, Lei Ling et al.
Europhysics Letters (EPL)
Neural Networks and Applications
article

LIC-free topology identification of complex dynamic networks

Xiaoqun Wu, Yi Zheng, Hao Liao, Lei Ling, Kong Yu-Peng
article en

Abstract

Abstract Over the past decade, synchronization-based identification methods have become a commonly used approach for identifying unknown network topologies. Most existing identification methods rely on the linear independence condition (LIC), which has been shown to have certain limitations. Recently, several new methods have been proposed to overcome this constraint. Building on existing methods, this paper proposes a series of novel predefined signals as drive network. Based on the proposed predefined signals, we develop a novel topology identification method and establish a new proof framework to theoretically validate the effectiveness of our method. Through the designed controllers and update laws, synchronization between the drive and auxiliary networks is achieved, thereby enabling the estimated matrix to accurately identify the unknown topology matrix. Numerical simulations demonstrate the effectiveness of the proposed method and validate the impact of strength selection for the predefined signals on identification performance.

Europhysics Letters (EPL)Vol. 152(3)
Shenzhen University (CN), Wuhan University (CN)
Openalex Percentile: Top 85%
Neural Networks and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.