Synchronization for stochastic semi-Markov jump neural networks with dynamic event-triggered scheme

This paper focuses on synchronization for stochastic semi-Markov jump neural networks with time-varying delay via dynamic event-triggered scheme. The neural networks under consideration are described by Ito^ stochastic differential equations with semi-Markov jump parameters. First, supplementary var...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2023-11, Vol.360 (16), p.12620-12639
Main Authors: Cao, Dianguo, Jin, Yujing, Qi, Wenhai
Format: Article
Language:English
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Summary:This paper focuses on synchronization for stochastic semi-Markov jump neural networks with time-varying delay via dynamic event-triggered scheme. The neural networks under consideration are described by Ito^ stochastic differential equations with semi-Markov jump parameters. First, supplementary variable technique and plant transformation are adopted to convert a phase-type semi-Markov process into an associated Markov process. Second, through stochastic analysis method and LaSalle-type invariance principle, novel sufficient conditions are deduced to realize stochastic synchronization for semi-Markov jump neural networks. Third, less conservative results are obtained compared with the existing methods. Finally, an industrial four-barrel model is applied to validate the superiority of the main algorithm.
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2021.07.058