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- W3196397733 abstract "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." @default.
- W3196397733 created "2021-09-13" @default.
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- W3196397733 date "2021-08-01" @default.
- W3196397733 modified "2023-10-10" @default.
- W3196397733 title "Synchronization for stochastic semi-Markov jump neural networks with dynamic event-triggered scheme" @default.
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- W3196397733 doi "https://doi.org/10.1016/j.jfranklin.2021.07.058" @default.
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