Fast Fixed-Time Control for Intra- and Inter-Layer Synchronization of Multi-Layer Networks

This study addresses the problem of fast fixed-time synchronization (FDTS) in multi-layer networks (MLNs), focusing on both intra-layer and inter-layer dynamic synchronization. First, we propose a more representative MLN framework in which each layer has a distinct topological structure, and node-to-node connections are established between corresponding nodes across layers. Second, we design a fast fixed-time controller to achieve synchronization of MLNs and estimate the synchronization time. Third, by applying the Lyapunov method and comparison theorem, we derive several novel synchronization conditions that are independent of initial states. Finally, numerical simulations are conducted to validate the theoretical findings.

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Publication Details

Journal
Axioms
Published
2025-10-28
DOI
https://doi.org/10.3390/axioms14110795
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Fast Fixed-Time Control for Intra- and Inter-Layer Synchronization of Multi-Layer Networks

Man-Fai Leung, Shiju Yang, Ying Tang, Yan Li et al.
Axioms
Neural Networks Stability and Synchronization
article

Fast Fixed-Time Control for Intra- and Inter-Layer Synchronization of Multi-Layer Networks

Man-Fai Leung, Shiju Yang, Ying Tang, Yan Li, Hong He
article en

Abstract

This study addresses the problem of fast fixed-time synchronization (FDTS) in multi-layer networks (MLNs), focusing on both intra-layer and inter-layer dynamic synchronization. First, we propose a more representative MLN framework in which each layer has a distinct topological structure, and node-to-node connections are established between corresponding nodes across layers. Second, we design a fast fixed-time controller to achieve synchronization of MLNs and estimate the synchronization time. Third, by applying the Lyapunov method and comparison theorem, we derive several novel synchronization conditions that are independent of initial states. Finally, numerical simulations are conducted to validate the theoretical findings.

AxiomsVol. 14(11)
Chongqing Technology and Business University (CN), Anglia Ruskin University (GB)
Openalex Percentile: Top 63%
Neural Networks Stability and Synchronization
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