@article{Feng2025, 
author = {Zongying Feng and Guoqiang Tan},
title = {Dynamic event-triggered        H          ∞       control for neural networks with sensor saturations and stochastic deception attacks},
year = {2025},
journal = {Electronic Research Archive},
volume = {33},
number = {3},
pages = {1267-1284},
keywords = {event-triggered scheme, neural networks, sensor saturation, quantization, cyber-attacks},
url = {https://www.sciopen.com/article/10.3934/era.2025056},
doi = {10.3934/era.2025056},
abstract = {This paper is devoted to dealing with the dynamic event-triggered        H          ∞       quantized control for neural networks with sensor saturations and stochastic deception attacks. To save the limited network resources, a dynamic event-triggered scheme is offered, which includes the general one. And a lower trigger frequency can be obtained by appropriately adjusting the triggering error. Then, a new closed-loop quantized control model is established under a dynamic event-triggered scheme, sensor saturations, and stochastic deception attacks, which is described by two independent Bernoulli-distributed variables. Moreover, by Lyapunov-Krasovskii functional theory, a new        H          ∞       performance criterion is given, and based on the criterion, the controller design approach is derived. Finally, simulations are listed to verify the validity of derived methods.}
}