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Finite-time Synchronization of Delayed Semi-Markov Neural Networks with Dynamic Event-triggered Scheme

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  • Intelligent Control and Applications
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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.

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Authors and Affiliations

  1. School of Engineering, Qufu Normal University, Rizhao, 276826, China

    Yujing Jin, Wenhai Qi & Guangdeng Zong

  2. School of Information Science and Engineering, Chengdu University, Chengdu, 610106, China

    Wenhai Qi

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  1. Yujing Jin
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Correspondence to Guangdeng Zong.

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This work was supported by the National Natural Science Foundation of China under Grant 62073188, Grant 61703231, Grant 61773235, Grant 61773236, and Grant 61873331, by the Natural Science Foundation of Shandong under Grant ZR2019YQ29, by the Postdoctoral Science Foundation of China under Grant 2018T110670, by the Taishan Scholar Project of Shandong Province under Grant TSQN20161033, and by the Interdisciplinary Scientific Research Projects of Qufu Normal University under Grant xkjjc201905.

Yujing Jin received her B.S. degree in applied mathematics from Binzhou university, Binzhou, China in 2018. She is currently pursuing an M.S. degree with Qufu Normal university, Qufu, China. Her current research interests include nonlinear control, neural networks and control theory.

Wenhai Qi was born in Tai’an, China, in 1986. He received his B.S. degree in automation and his M.S. degree in control theory and control engineering from Qufu Normal University, Jining, China, in 2008 and 2013, respectively, and his Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2016. From July 2018 to August 2018, he visited the Department of Electrical Engineering, Yeungnam University, Gyeongsan, Korea. From December 2019 to January 2020, he visited the Department of Mechanical Engineering, The University of Hong Kong, Hong Kong. He is currently working with the School of Engineering, Qufu Normal University, Rizhao, China. His research interests include Markov jump systems, switched systems, positive systems, and networked control systems. Dr. Qi is currently an Editorial Board Member for some international journals, such as the International Journal of Control, Automation, and Systems and the Journal of Information Processing Systems.

Guangdeng Zong was born in Linyi, China, in 1976. He received his M.S. degree in mathematics from Qufu Normal University, Qufu, China, in 2002, and his Ph.D. degree in control theory and control engineering from the Control Science and Engineering Department, School of Automation, Southeast University, Nanjing, China, in 2005. He was a Post-Doctoral Fellow with the School of Automation, Nanjing University of Science and Technology, Nanjing, from 2006 to 2009. In 2010, he was a Visiting Professor with the Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA. In 2012, he was a Visiting Fellow with the School of Computing, Engineering and Mathematics, University of Western Sydney, Penrith, NSW, Australia. In 2016, he was a Visiting Professor with the Institute of Information Science, Academia Sinica, Taipei, Taiwan. From 2018 to 2019, he visited the Department of Mechanical Engineering, The University of Hong Kong, Hong Kong. He is currently a Full Professor with Qufu Normal University, Rizhao, China. Dr. Zong is currently an Editorial Board Member for some international journals, such as IEEE ACCESS, the International Journal of Control, Automation, and Systems, and the Journal of Machines.

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Jin, Y., Qi, W. & Zong, G. Finite-time Synchronization of Delayed Semi-Markov Neural Networks with Dynamic Event-triggered Scheme. Int. J. Control Autom. Syst. 19, 2297–2308 (2021). https://doi.org/10.1007/s12555-020-0348-2

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