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

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.

Article

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.

IJCAS
February 2024

Vol. 22, No. 2, pp. 347~729

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