Random Time-Space Sampled-Data Control of T-S Fuzzy Reaction-Diffusion Neural Networks With Time-Delayed Communication Scheme

This study focuses on TSSDC for Takagi-Sugeno (T-S) fuzzy reaction-diffusion neural networks (NNs) under random sampling and network-induced delays. Fuzzy reaction-diffusion NNs extend traditional NNs by incorporating spatial dynamics through reaction-diffusion processes, offering improved modeling capabilities for complex systems. However, the combination of reaction-diffusion terms and fuzzy modeling increases the complexity of dynamic analysis, particularly in synchronization control. To address these challenges, a novel random TSSDC (RTSSDC) framework is developed. The proposed approach integrates random sampling across both temporal and spatial dimensions with an innovative random event-triggered communication scheme to increase resource utilization efficiency and accommodate real-world network conditions. A unified closed-loop model is established by introducing a packet loss scheduling strategy to handle data disorder caused by significant transmission delays. The framework incorporates switching gains to provide additional flexibility and robustness. Ultimately, numerical simulations are conducted to validate the superior synchronization performance and efficient resource utilization under random sampling and network-induced delays.