Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on system dynamics and utilize combination FTS communication to mitigate delays, error, efficient synchronization. We analyze convergence properties proposed method provide simulation results demonstrate its effectiveness in synchronizing network QMJTDNNs. introduce new achieve that has input constraints. involves development Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, some sufficient conditions, all which are expressed as linear matrix inequalities (LMIs). Furthermore, study presents design an controller gain larger sampling interval. is demonstrated through numerical examples.