Decentralized dynamic event-triggered passive bipartite synchronization for semi-Markov jump cooperation-competition neural networks under hybrid random cyber-attacks

The issue of bipartite synchronization for a class of continuous-time coupled neural networks is investigated in this article, in which the interactions among the neural network nodes coexist collaboratively and antagonistically. At first, the semi-Markov jump process is utilized to model the stochastic switching network topology. Then, a decentralized dynamic event-triggered mechanism incorporating a novel dynamic threshold parameter is proposed to avoid unnecessary continuous monitoring and reduce communication overhead. Besides, the secure bipartite synchronization controller is devised to meet the control demand under hybrid cyber-attacks. Thereafter, according to the Lyapunov stability theory, sufficient conditions are developed to guarantee that the resulting error system is stochastically stable with the specified passive performance. Lastly, the effectiveness of the proposed controller is validated through a simulation example.