Finite-time event-triggered approach for recurrent neural networks with leakage term and its application

R. Vadivel, Porpattama Hammachukiattikul, G. Rajchakit, M. Syed Ali and Bundit Unyong

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 182, issue C, 765-790

Abstract: This work investigates the finite-time event-triggered approach for recurrent neural networks with leakage term and its application. Here, decentralized event-triggered framework is recommended where event is checked at every sensor node related to local information for available triggering and the updated control is done whenever a centralized event is triggered. By handling the Lyapunov–Krasovskii functional (LKF) method together with novel inequality techniques like Wirtinger single and double integral inequality (WSI,WDI) technique, delay productive term (DPT), and a few adequate conditions are acquired to ensure the finite-time stability (FTS) analysis for the considered system, which is expressed with respect to linear matrix inequalities (LMIs). At last, numerical simulations are provided to indicate the efficiency of the expected results, two of these examples were supported by genuine use of the benchmark issue that correlates with sensible concerns under finite-time execution.

Keywords: Event-triggered; Leakage delay; Lyapunov–Krasovskii functional; Linear matrix inequality; Finite-time stability (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:182:y:2021:i:c:p:765-790

DOI: 10.1016/j.matcom.2020.12.001

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