 A memory behavior related hybrid event-triggered mechanism for an improved robust control on neural networksYang Liu, Zhenzhen Zhang, Hao Chen and Shouming Zhong Mathematics and Computers in Simulation (MATCOM), 2023, vol. 212, issue C, 1-20 Abstract: This paper addresses an H∞ control approach on neural networks with hybrid-triggered mechanism (HTM) under deception attacks. With the aim of mitigating the burden of the transmission network, an HTM is introduced to handle unforeseen non-ideal environment influence, which is characterized by Bernoulli distribution. The weight combination coefficients ϵj related to historical information are conducted to develop an improved HTM. By taking into account network-induced delay, and the randomly happened deception attacks in transmission network, a Lyapunov–Krasovskii functional (LKF) is constructed. Using linear matrix inequality (LMI), sufficient conditions are formed to render the system asymptotically stable and the H∞ hybrid-triggered controller is designed. Finally, simulation examples are executed to validate the feasibility of the developed method. Keywords: Neural networks; Hybrid-triggered mechanism; Deception attacks; Asymptotically stable (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:212:y:2023:i:c:p:1-20 DOI: 10.1016/j.matcom.2023.04.023 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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