Fixed-Time Stabilization of BAM Neural Networks with Mixed-Time Delays via Event-Triggered Control

This paper focused on the fixed-time stabilization (FTS) of bidirectional associative memory memristive neural networks (BAMMNNs) with mixed time delays under event-triggered control (ETC) strategies. It introduced derivative- weighted switching to more precisely adjust weights to reduce oscillations and enhance the network's adaptability. Based on the dual-layer structure of BAMMNNs, we designed two similar controllers and a segmented ETC strategy that triggers control actions only at specific nodes, thereby reducing the transmission burden. Additionally, a new FTS lemma is established based on previous research achievements. By combining Lyapunov stability theory and inequality techniques, the FTS results of BAMMNNs are proved. Finally, numerical simulations are presented to demonstrate the effectiveness of the conclusions.