Synchronization Control for Discrete-Time Fuzzy Complex Networks: A Novel State-Dependent Intermittent Memory-Event-Triggered Scheme

In this study, the synchronization problem of discrete-time fuzzy complex networks (FCNs) is investigated based on a state-dependent intermittent memory-event-triggered (MET) scheme. To alleviate the burden of network communication, a state-dependent-region-partitioning-based intermittent mechanism is proposed to set the working/rest interval of the controller. Compared with the existing results, it realizes on-demand control, reduces resource consumption, and expands applicability. A MET mechanism that depends on historical triggering states is developed during the working interval, which adds “smarter” to the triggering decision. Besides, a working-state-dependent threshold function is designed in the triggering condition, which realizes the correlation between intermittent and event-triggered mechanisms. Compared with the existing results, the mechanism provides intelligent regulation of the triggering instant, thereby optimizing the controller update instant and control effect, achieving the goal of improving the control effect while reducing the triggering frequency. Sufficient criteria for achieving synchronization of discrete-time FCNs are derived using Lyapunov stability theory under the state-dependent intermittent MET scheme. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

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Institutions

Publication Details

Journal
IEEE Transactions on Fuzzy Systems
Published
2025-08-01
DOI
https://doi.org/10.1109/tfuzz.2025.3570996
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.49

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article

Synchronization Control for Discrete-Time Fuzzy Complex Networks: A Novel State-Dependent Intermittent Memory-Event-Triggered Scheme

Zhihong Liang, Qiongwen Zhang, Juan Zhang, Huaguang Zhang
1 citations
IEEE Transactions on Fuzzy Systems
Neural Networks Stability and Synchronization
1.49
article

Synchronization Control for Discrete-Time Fuzzy Complex Networks: A Novel State-Dependent Intermittent Memory-Event-Triggered Scheme

Zhihong Liang, Qiongwen Zhang, Juan Zhang, Huaguang Zhang
article en
1 citations

Abstract

In this study, the synchronization problem of discrete-time fuzzy complex networks (FCNs) is investigated based on a state-dependent intermittent memory-event-triggered (MET) scheme. To alleviate the burden of network communication, a state-dependent-region-partitioning-based intermittent mechanism is proposed to set the working/rest interval of the controller. Compared with the existing results, it realizes on-demand control, reduces resource consumption, and expands applicability. A MET mechanism that depends on historical triggering states is developed during the working interval, which adds “smarter” to the triggering decision. Besides, a working-state-dependent threshold function is designed in the triggering condition, which realizes the correlation between intermittent and event-triggered mechanisms. Compared with the existing results, the mechanism provides intelligent regulation of the triggering instant, thereby optimizing the controller update instant and control effect, achieving the goal of improving the control effect while reducing the triggering frequency. Sufficient criteria for achieving synchronization of discrete-time FCNs are derived using Lyapunov stability theory under the state-dependent intermittent MET scheme. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

IEEE Transactions on Fuzzy SystemsVol. 33(8)
Foshan University (CN), Northeastern University (CN)
National Natural Science Foundation of China, Liaoning Revitalization Talents Program, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 14%
Neural Networks Stability and Synchronization
1.49
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Synchronization Control for Discrete-Time Fuzzy Complex Networks: A Novel State-Dependent Intermittent Memory-Event-Triggered Scheme — Zhihong Liang, Qiongwen Zhang, et al. · IEEE Transactions on Fuzzy Systems (2025) | TGRS Research Map | TGRS