Adaptive Event-Triggered Synchronization of Reaction–Diffusion Neural Networks

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from existing ETSDC schemes predetermined constant thresholds, is proposed RDNNs. The can be promptly adaptively adjusted since threshold function based current sampled and latest transmitted signals. Thus, effectively save communication resources By taking influence uncertain factors, delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing appropriate Lyapunov-Krasovskii functional (LKF), new criteria derived solving set linear matrix inequalities (LMIs), desired gain obtained. Finally, merits effectiveness results verified one numerical example.