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 timevarying delay via dynamic event-triggered scheme. The neural networks under consideration are described by It o<SIC> 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. (c) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.