Output-feedback synchronization of semi-Markov jump two-time-scale neural networks: Dual event-triggered scheme

초록

This paper addresses the problem of synchronization control for semi-Markov jump two-time-scale neural networks, in which the output-feedback mechanism is adopted and a dual event-triggered scheme is employed using a double-rate sampling method to balance system performance and communication efficiency. First, considering the two-time-scale property of the semi-Markov jump neural networks, the dual-rate sampling strategy is adopted such that two independent event-triggered conditions for different time scales can be designed, which ensure efficient resource utilization while maintaining system performance. Then, a Lyapunov-Krasovskii functional with the singular perturbation parameter is constructed to deduce sufficient conditions ensuring that the synchronization error system is stochastically stable and satisfies a given HPo performance index. Moreover, the solution for obtaining the controller gains is presented to guarantee synchronization of the considered system under a dual event-triggered scheme. Finally, the feasibility of the methods is demonstrated by two examples, including a numerical example and an image encryption. They show that this event-triggered mechanism provides an efficient new synchronization control scheme for semi-Markov jump two-time-scale neural network systems while reducing the network burden.

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