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

Automated summary

In this paper, a finite-time synchronization method is proposed for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The method takes into account the effects of quantization and uses a combination of finite-time synchronization and event-triggered communication to achieve efficient synchronization. The proposed method is analyzed for its finite-time synchronization and convergence properties, and simulation results demonstrate its effectiveness in synchronizing a network of QMJTDNNs. A new method for achieving finite-time synchronization of a system with input constraints is introduced, which involves the use of Lyapunov-Krasovskii functional approach, integral inequality techniques, and linear matrix inequalities. Additionally, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is verified through numerical examples.