Dynamic event triggering output feedback synchronization for Markov jump neural networks with mode detection information

This article investigates the synchronization control problem of discrete-time Markov jump neural networks. Because of the possible mismatch of controller mode information and the difficulty in obtaining neuron information in practical environments, a hidden Markov model is introduced, which contains a partially unknown detection probability matrix and a partially unknown transition probability matrix. To overcome the unpredictability of the system state and enhance the effective utilization of communication resources, a static output feedback controller based on a dynamic event triggering strategy is designed. Moreover, the conservatism of theoretical derivation is further reduced through the activation function division. Finally, numerical examples are used to verify the reliability of the above results, which are then applied to image encryption.