Topology Identification of Multilayer Complex Dynamical Networks with Multiple Weights Considering Derivative Coupling

This paper addresses the topology identification problem of multilayer complex dynamical networks with multiple weights, considering derivative coupling. First, we propose a multilayer complex dynamical network model with multiple weights that includes derivative coupling. Using the drive-response method, we construct the response network and derive the error dynamical equation for both the drive and response networks. Next, we design an appropriate controller and topology identifiers for the multilayer complex dynamical networks with multiple weights. By applying Lyapunov stability theory, we ensure the stability of the error system, which enables the topology identification of the drive network and allows us to determine its structure. Finally, the theoretical results of this paper are validated through numerical simulations.

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Publication Details

Published
2024-11-01
DOI
https://doi.org/10.1109/cac63892.2024.10865525
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Topology Identification of Multilayer Complex Dynamical Networks with Multiple Weights Considering Derivative Coupling

Guo‐Ping Jiang, Lu Hao, Yayong Wu, Ying Zheng
Neural Networks and Applications
article

Topology Identification of Multilayer Complex Dynamical Networks with Multiple Weights Considering Derivative Coupling

Guo‐Ping Jiang, Lu Hao, Yayong Wu, Ying Zheng
article en

Abstract

This paper addresses the topology identification problem of multilayer complex dynamical networks with multiple weights, considering derivative coupling. First, we propose a multilayer complex dynamical network model with multiple weights that includes derivative coupling. Using the drive-response method, we construct the response network and derive the error dynamical equation for both the drive and response networks. Next, we design an appropriate controller and topology identifiers for the multilayer complex dynamical networks with multiple weights. By applying Lyapunov stability theory, we ensure the stability of the error system, which enables the topology identification of the drive network and allows us to determine its structure. Finally, the theoretical results of this paper are validated through numerical simulations.

Nanjing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 75%
Neural Networks and Applications
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