Regular Papers
International Journal of Control, Automation and Systems 2021; 19(6): 2297-2308
Published online March 30, 2021
https://doi.org/10.1007/s12555-020-0348-2
© The International Journal of Control, Automation, and Systems
Finite-time Synchronization of Delayed Semi-Markov Neural Networks with Dynamic Event-triggered Scheme
In this paper, the finite-time synchronization (FTS) of semi-Markov neural networks (S-MNNs) with time-varying delay is presented. According to the Lyapunov stability theory, a mode-dependent Lyapunov Krasovskii functional (LKF) is constructed. Compared with the traditional static event triggered scheme (ETS), a dynamic ETS is adopted to adjust the amount of data transmission and reduce the network burden. By using the general free-weighting matrix method (F-WMM) to estimate a single integral term, a less conservative conclusion is proposed in standard linear matrix inequalities (LMIs). Finally, under the comparison of the static ETS and the dynamic ETS, a simulation example verifies the superiority of this method.
Keywords Dynamic event-triggered scheme, finite-time synchronization, Lyapunov-Krasovskii functional, semi-Markov neural networks.
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Regular Papers
International Journal of Control, Automation and Systems 2021; 19(6): 2297-2308
Published online June 1, 2021 https://doi.org/10.1007/s12555-020-0348-2
Copyright © The International Journal of Control, Automation, and Systems.
Finite-time Synchronization of Delayed Semi-Markov Neural Networks with Dynamic Event-triggered Scheme
Yujing Jin, Wenhai Qi, and Guangdeng Zong*
Qufu Normal University
Abstract
In this paper, the finite-time synchronization (FTS) of semi-Markov neural networks (S-MNNs) with time-varying delay is presented. According to the Lyapunov stability theory, a mode-dependent Lyapunov Krasovskii functional (LKF) is constructed. Compared with the traditional static event triggered scheme (ETS), a dynamic ETS is adopted to adjust the amount of data transmission and reduce the network burden. By using the general free-weighting matrix method (F-WMM) to estimate a single integral term, a less conservative conclusion is proposed in standard linear matrix inequalities (LMIs). Finally, under the comparison of the static ETS and the dynamic ETS, a simulation example verifies the superiority of this method.
Keywords: Dynamic event-triggered scheme, finite-time synchronization, Lyapunov-Krasovskii functional, semi-Markov neural networks.
Vol. 22, No. 9, pp. 2673~2953
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