Dynamic-Memory Protocol-Based Synchronization for Semi-Markov Jump Reaction-Diffusion CDNs

This study investigates the synchronization of reaction-diffusion complex dynamical networks (CDNs) based on semi-Markov switching topology and an event-triggered protocol. The investigated model is rendered more practical via the introduction of a semi-Markov process for stochastic jump CDNs. Based on the internal dynamic variable history information, a dynamic-memory event-triggered strategy is proposed, wherein the primary novelty lies in its prior transmitted packets to enhance the control performance. This further reduces data transmission based on the dynamic threshold parameters. The Bessel-Legendre inequality is adopted to reduce the conservatism of the obtained results. In addition, sufficient synchronization conditions are established to ensure the stochastic stability of the error system for two different models (partial differential equations-and ordinary differential equations-based models). Furthermore, two examples are provided to illustrate the effectiveness of the theoretical results.

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Publication Details

Journal
IEEE Transactions on Cybernetics
Published
2024-12-03
DOI
https://doi.org/10.1109/tcyb.2024.3502684
Citations
4
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.40

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article

Dynamic-Memory Protocol-Based Synchronization for Semi-Markov Jump Reaction-Diffusion CDNs

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4 citations
IEEE Transactions on Cybernetics
Neural Networks Stability and Synchronization
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article

Dynamic-Memory Protocol-Based Synchronization for Semi-Markov Jump Reaction-Diffusion CDNs

Jinde Cao, Guangdeng Zong, Shan Jin, Wenhai Qi, Huaicheng Yan, Jun Cheng, Zhenzhen Yuan
article en
4 citations

Abstract

This study investigates the synchronization of reaction-diffusion complex dynamical networks (CDNs) based on semi-Markov switching topology and an event-triggered protocol. The investigated model is rendered more practical via the introduction of a semi-Markov process for stochastic jump CDNs. Based on the internal dynamic variable history information, a dynamic-memory event-triggered strategy is proposed, wherein the primary novelty lies in its prior transmitted packets to enhance the control performance. This further reduces data transmission based on the dynamic threshold parameters. The Bessel-Legendre inequality is adopted to reduce the conservatism of the obtained results. In addition, sufficient synchronization conditions are established to ensure the stochastic stability of the error system for two different models (partial differential equations-and ordinary differential equations-based models). Furthermore, two examples are provided to illustrate the effectiveness of the theoretical results.

IEEE Transactions on CyberneticsVol. 55(2)
East China University of Science and Technology (CN), Tiangong University (CN), Qufu Normal University (CN), Guangxi Normal University (CN), Southeast University (CN), China Medical University (CN)
National Natural Science Foundation of China, Taishan Scholar Project of Shandong Province
Affordable and clean energy
Openalex Percentile: Top 16%
Neural Networks Stability and Synchronization
1.40
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