Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks
Xiulin Wang, Youzhi Cai, Feng Li · Chinese Physics B · 2024
Abstract This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks, in which the Markov process is used to model the mixed cyberattacks. To optimize the utilization of channel resources, a decentralized event-triggered mechanism is adopted during the information transmission. By establishing the augmentation system and constructing the Lyapunov function, sufficient conditions are obtained for the system to be finite-time bounded and satisfy the H ∞ performance index. Then, under these conditions, a suitable state estimator gain is obtained. Finally, the feasibility of the method is verified by a given illustrative example.