Sampled-data Stabilization of Delayed Memristor-based Neural Networks with Communication Delays via Continuous-time Model

This article investigates the stabilization of memristor-based neural networks with communication delays and system delays under sampled-data control. Firstly, delayed memristor-based neural networks are modeled as continuous-time systems in the flux-voltage-time domain. Secondly, sampled-data control with communication delays is proposed. Thirdly, by input delay approach, interval delays method, integral inequalities, and novel functionals, stability criteria are obtained. Finally, a simulation example is given to verify the effectiveness of the results.