Event-Triggered Quantized Input-Output Finite-Time Synchronization of Markovian Neural Networks
Peng Shi, Xiao Li, Yingqi Zhang, Jingjing Yan
Abstract
This paper addresses the event-triggered input-output finite-time mean square synchronization for uncertain Markovian jump neural networks with partly unknown transition rates and quantization. Considering the limited network resources, an event-triggered technique and a logarithmic quantizer are both provided. The error system model with uncertainty is established in the unified framework. Then, based on Lyapunov functional approach, interesting results are presented to guarantee the properties of the input-output finite-time mean square synchronization for the error systems. Furthermore, some solvability conditions are induced for the desired input-output finite-time mean square synchronization controller under linear matrix inequality techniques. Eventually, the theoretical finding’s efficiency is shown by an example.