Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities

This study develops the space-time sampled-data control problem for memristor-based reaction-diffusion neural networks (MRDNNs) using a memory event-triggering scheme. Unlike traditional Markov switching models with fixed transition probabilities, the nonhomogeneous sojourn probabilities are capable of describing the switching behavior more accurately. By effectively leveraging historical transmitted packets, an innovative mode-dependent memory event-triggering scheme that incorporates nonhomogeneous sojourn probability information is introduced, optimizing communication resource utilization. A space-time sampled-data control law is designed by sampling both spatial and temporal domains, significantly reducing network communication resource consumption while achieving the desired system performance. The validity and superiority of the proposed space-time sampled-data control strategy are demonstrated through a simulation example.