Synchronization for Markovian master-slave neural networks: an event-triggered impulsive approach

Abstract

This paper investigates synchronisation for Markovian master-slave neural networks (NNs), where the transition probabilities of Markov chain are partially unknown and uncertain. To cope with the communication channel bandwidth constraint, an event-triggered impulsive transmission strategy is adopted, a corresponding impulsive controller is then designed. In this method, information transmission occurs only at some discontinous instants, which are determined by a state-dependent event-triggered condition as well as a predesigned forced impulse interval. Synchronization for Markovian master-slave NNs is guaranteed by a sufficient condition, and the controller gains are designed by using the obtained results. A numerical simulation is given to show the effectiveness of the presented method.

Keywords:

• Synchronization
• neural networks
• Markov chain
• event-triggered impulsive control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

There are no exogenous data sets in this work.

Additional information

Funding

This work was supported in part by Key Area Research and Development Program of Guangdong Province [grant number 2021B0101410005], the Natural Science Foundation of Guangdong Province, China [grant numbers 2021B1515420008, 2021A1515011634], the National Natural Science Foundation of China [grant numbers 62121004, 62006043, 62027817], and the Science and Technology Program of Guangzhou, China [grant number 202102020639].

Notes on contributors

Yumei Zhou

Yumei Zhou received the B.S. degree from Guangxi normal University, Guilin, China, in 2012, and the M.S. degree in electronic science and technology from the Guangdong University of Technology, Guangzhou, China, in 2015, where she is working toward her Ph.D. in control science and engineering.

Yuru Guo

Yuru Guo was born in Hubei province, China,in 1997. She received the B.S. degree from the school of electrical engineering, Nanjing Institute of Technology, Jiangsu, China, in 2019. She is currently working toward the PhD degree in Control Science and Engineering at Guangdong University of Technology, Guangzhou, China. Her research focuses on the adaptive impulsive control of networked systems.

Chang Liu

Chang Liu received the B.S. degree in automation in  2016 from Henan University, Kaifeng, China, and the M.S. degree in control engineering in 2019 and the Ph.D. degree in control science and engineering in 2022, both from Guangdong University of Technology, Guangzhou, China. He is currently a Post-Doctoral Scholar with the school of Automation, Guangdong University of Technology, Guangzhou, China. His current research interests include networked control systems, neural networks, intermittent control, and set-membership filtering.

Hui Peng

Hui Peng received the B.S. degree in automation and the Ph.D. degree in control science and engineering from Hangzhou Dianzi University, Hangzhou, China, in 2013 and 2018, respectively. She is currently a Lecturer with the School of Automation, Guangdong University of Technology, Guangzhou, China.

Hongxia Rao

Hongxia Rao was born in Jiangsu, China, in 1986. She received the B.S. degree from Nanchang Hangkong University, Nanchang, China, in 2007, the M.S. degree from the Nanjing University of Science and Technology, Nanjing, China, in 2009, and the Ph.D. degree in control science and engineering from the Guangdong University of Technology, Guangzhou, China, in 2019.