Probabilistic-sampling-based asynchronous control for semi-Markov jumping neural networks with reaction-diffusion terms.

Abstract

This paper investigates the probabilistic-sampling-based asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs). Aiming at mitigating the drawback of the well-known fixed-sampling control law, a more general probabilistic-sampling-based control strategy is developed to characterize the randomly sampling period. The system mode is considered to be related to the sojourn-time and undetectable. The jumping of the controller depends on the observation mode, and is asynchronous with the jumping of the system mode. By utilizing the established hidden semi-Markov model and a stochastic analysis approach, some sufficient conditions are obtained to ensure the asymptotically stable of the SMRDNNs. Finally, an example is given to prove the validity and superiority of the conclusion.