Adaptive Synchronization of Uncertain Topological Connected Drive-Response Complex Dynamical Networks

This paper probes the synchronization matter of unascertained drive-response complicated dynamical nets under distributed time-varying delays. A new class of T-S fuzzy model is proposed to deal with the unknown topological structure in this paper. Then, the unknown drive-response network is presented by a fuzzy drive-response network. The asymptotic synchronization and lag synchronization of the fuzzy drive-response networks are studied in this paper. In order to achieve synchronization of the above fuzzy drive-response network, this paper designs fuzzy adaptive feedback controllers which is only connected with the dynamical behavior of directly related nodes. The sufficient synchronization conditions are derived in term of Linear Matrix Inequality (LMI). Numeric examples are shown to prove the effectiveness of the given results.

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Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/ddcls66240.2025.11065419
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Adaptive Synchronization of Uncertain Topological Connected Drive-Response Complex Dynamical Networks

Xinxin Yuan, Junmin Li, Jianguo Cui, Yueying Li
Neural Networks Stability and Synchronization
article

Adaptive Synchronization of Uncertain Topological Connected Drive-Response Complex Dynamical Networks

Xinxin Yuan, Junmin Li, Jianguo Cui, Yueying Li
article en

Abstract

This paper probes the synchronization matter of unascertained drive-response complicated dynamical nets under distributed time-varying delays. A new class of T-S fuzzy model is proposed to deal with the unknown topological structure in this paper. Then, the unknown drive-response network is presented by a fuzzy drive-response network. The asymptotic synchronization and lag synchronization of the fuzzy drive-response networks are studied in this paper. In order to achieve synchronization of the above fuzzy drive-response network, this paper designs fuzzy adaptive feedback controllers which is only connected with the dynamical behavior of directly related nodes. The sufficient synchronization conditions are derived in term of Linear Matrix Inequality (LMI). Numeric examples are shown to prove the effectiveness of the given results.

Xidian University (CN), Taiyuan University of Technology (CN)
Openalex Percentile: Top 82%
Neural Networks Stability and Synchronization
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