Abstract
This article addresses the mean-square exponential synchronization problem of reaction-diffusion neural networks (RDNNs) subject to stochastic switching and communication constraints. Different from existing Markov jump formulations that require exact transition probabilities or rely on constant sojourn probabilities, a dwell-time-dependent sojourn-probability switching rule is constructed to characterize random mode evolution in a more tractable form. To reduce the communication burden under random sampling, a random adaptive event-triggered protocol (RAETP) is developed, in which the triggering threshold is adjusted online according to the state error and the active sampling interval. Furthermore, a random spatiotemporal sampled-data control (RSTSDC) scheme is established by jointly introducing random temporal sampling, random spatial sampling, and switching gains into the RDNN synchronization framework. Based on this model, sufficient conditions are derived to guarantee mean-square exponential synchronization. Comparative simulations show that the proposed design achieves faster convergence and lower communication cost than several representative benchmark strategies.