L₂-L_∞ state estimation for continuous stochastic delayed neural networks via memory event-triggering strategy

Abstract

In this paper, we study the memory event-triggering $L_2$-$L_{\mathrm{\infty }}$ state estimation for a type of continuous stochastic neural networks (NNs) subject to time-varying delays. The information of some recent released packets is made use in the proposed triggering conditions to schedule the data propagation, thereby reducing communication frequency and saving energy. By taking into account network-induced complexities (i.e. transmission delays and random disturbances), we first formulate the evolutions of estimation error in an augmented form, and then propose the conditions under with the design goals could be met. By using certain novel Lyapunov–Krasovskii (L–K) functionals in combination with stochastic analysis technique, sufficient conditions have been provided for the existence of desired estimator, guaranteeing both the globally asymptotically mean-square stability and the prescribed $L_2$-$L_{\mathrm{\infty }}$ performance simultaneously. Moreover, the estimator gains are obtained by virtue of certain convex optimisation algorithms. Finally, we use an illustrative example to verify the obtained theoretical algorithm.

Keywords:

• Memory event-triggering
• $L_2$-$L_{\mathrm{\infty }}$ state estimation
• continuous stochastic neural networks
• time-varying delay

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Disclosure statement

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

Additional information

Notes on contributors

Juanjuan Yang

Juanjuan Yang received the B.Sc. degree in mathematics from the Henan University of Economics and Law, in 2011, and the M.Sc. degree in mathematics from Zhengzhou University, in 2014. She is currently working toward the Ph.D. degree in control science and engineering with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. Her research interests include time-delay systems and nonlinear system control and estimation, and networked control systems.

Lifeng Ma

Lifeng Ma received the B.Sc. degree in automation from Jiangsu University, Zhenjiang, China, in 2004 and the Ph.D. degree in control science and engineering from the Nanjing University of Science and Technology, Nanjing, China, in 2010. From August 2008 to February 2009, he was a Visiting Ph.D. Student with the Department of Information Systems and Computing, Brunel University London, London, U.K. From January 2010 to April 2010 and May 2011 to September 2011, he was a Research Associate with the Department of Mechanical Engineering, The University of Hong Kong, Hong Kong. From March 2015 to February 2017, he was a Visiting Research Fellow with King's College London, London, U.K. He is currently a Professor with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. He has authored or coauthored more than 50 papers in refereed international journals. His current research interests include control and signal processing, machine learning, and deep learning. Prof. Ma is currently the Editor of the Neurocomputing and International Journal of Systems Science.

Yonggang Chen

Yonggang Chen received the B.Sc. and M.Sc. degrees in mathematics from Henan Normal University, Xinxiang, China, in 2003 and 2006, respectively, and the Ph.D. degree in control theory and control engineering from Southeast University, Nanjing, China, in 2013. From April 2016 to April 2017, he was a Visiting Scholar with the Department of Computer Science, Brunel University London, London, U.K. He is currently an Associate Professor with the School of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang, China, and also with the School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, China. His research interests include time-delay systems, constrained control, neural networks, and switched system control.

Xiaojian Yi

Xiaojian Yi was born in 1987. He received the B.S. degree in control technology from the North University of China, Taiyuan, China, in 2010, and the M.S. and Ph.D. degrees in reliability engineering from the Beijing Institute of Technology, Beijing, China, in 2012 and 2016, respectively. During 2015–2016, he was a jointly trained Ph.D. student with the University of Ottawa, Ottawa, ON, Canada, to study robot reliability and maintenance. From 2016 to 2020, he was an Associate Professor with China North Vehicle Research Institute. He is currently an Associate Professor with the Beijing Institute of Technology. He is the author of two books and more than 100 articles, and is also the holder of eight patents. His research interests include system reliability analysis, intelligent control, fault diagnosis, and health management.