Event-Triggered Synchronization Control for Markov Jump Neural Networks With Partially Unknown Transition Probabilities

Abstract

This article studies the problem of static output feedback synchronization control of Markov jump neural networks. Given the randomness of the neural network topology and the limitations in acquiring transition probabilities, a Markov model with partially unknown transition probabilities is adopted, which aligns more closely with practical applications. To enhance communication efficiency in resource-constrained environments, an event-triggered mechanism is introduced. Additionally, in contrast to previous studies, this article employs the technique of free-weighting matrix to address the decoupling issue in such neural networks, significantly reducing the conservativeness of the static output feedback control strategy. Finally, the theoretical findings are validated through simulation, demonstrating the practical applicability and effectiveness of the theoretical results.