Abstract
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 $H_{\infty}$ 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.
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