Synchronization for Markovian master-slave neural networks: an event-triggered impulsive approach
Yumei Zhou, Yuru Guo, Chang Liu, Hui Peng, Hongxia Rao
Abstract
This paper investigates synchronisation for Markovian master-slave neural networks (NNs), where the transition probabilities of Markov chain are partially unknown and uncertain. To cope with the communication channel bandwidth constraint, an event-triggered impulsive transmission strategy is adopted, a corresponding impulsive controller is then designed. In this method, information transmission occurs only at some discontinous instants, which are determined by a state-dependent event-triggered condition as well as a predesigned forced impulse interval. Synchronization for Markovian master-slave NNs is guaranteed by a sufficient condition, and the controller gains are designed by using the obtained results. A numerical simulation is given to show the effectiveness of the presented method.