@article{Zamart2023, 
author = {Chantapish Zamart and Thongchai Botmart and Wajaree Weera and Prem Junsawang},
title = {Finite-time decentralized event-triggered feedback control for generalized neural networks with mixed interval time-varying delays and cyber-attacks},
year = {2023},
journal = {AIMS Mathematics},
volume = {8},
number = {9},
pages = {22274-22300},
keywords = {generalized neural networks, finite-time stability, time-varying delays, feedback control, cyber-attacks, decentralized event-triggered scheme},
url = {https://www.sciopen.com/article/10.3934/math.20231136},
doi = {10.3934/math.20231136},
abstract = {This article investigates the finite-time decentralized event-triggered feedback control problem for generalized neural networks (GNNs) with mixed interval time-varying delays and cyber-attacks. A decentralized event-triggered method reduces the network transmission load and decides whether sensor measurements should be sent out. The cyber-attacks that occur at random are described employing Bernoulli distributed variables. By the Lyapunov-Krasovskii stability theory, we apply an integral inequality with an exponential function to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). We present new sufficient conditions in the form of linear matrix inequalities. The main objective of this research is to investigate the stochastic finite-time boundedness of GNNs with mixed interval time-varying delays and cyber-attacks by providing a decentralized event-triggered method and feedback controller. Finally, a numerical example is constructed to demonstrate the effectiveness and advantages of the provided control scheme.}
}