Distributed mode-dependent state estimation for semi-Markovian jumping neural networks via sampled data

Abstract

In this paper, a novel distributed state estimation scheme with sampled data is proposed for the semi-Markovian jumping neural networks (SMJNNs) with time-varying delays. In particular, mode-dependent distributed state estimators are designed to provide more flexibility. Based on the mode-dependent Lyapunov-Krasovskii functional, sufficient criteria are presented for ensuring the existence of the state estimators, based on which the desired mode-dependent estimator gains are further obtained. Finally, an illustrative example is presented for verifying the effectiveness and applicability of our theoretical results.

Keywords:

• Distributed state estimation
• semi-Markovian jumping neural networks
• sampled data

Acknowledgments

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions that have improved the presentation of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61703038, 61627808, 31200829]; the Fundamental Research Funds for the Central Universities [grant number FRF-TP-15-115A1].

Notes on contributors

Chao Ma

Chao Ma received the B.S. degree in automation from Central South University, Changsha, China, in 2007, the M.S. degree and the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2010 and 2015. Currently he is a lecturer at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, P.R. China. His research interests include intelligent robots, intelligent agents and hybrid systems.

Wei Wu

Wei Wu received the B.Sc. degree in physics and M.Sc. degree in theoretical physics from Beijing Normal University, Beijing, China, in 2001 and 2004, respectively, and the Ph.D. degree in computational neuroscience from Johann Wolfgang Goethe University, Frankfurt, Germany, in 2008. He is currently an Associate Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing. His research interests include artificial intelligence and complex networks.

Yinlin Li

Yinlin Li received the B.S. degree in measurement & control technology and instrumentation from Xidian University, Xi'an, China, in 2011 and the Ph.D degree in pattern recognition and intelligent system with the Institute of Automation, Chinese Academy of Sciences, Beijing, China. She is currently an Assistant Professor with Institute of Automation, Chinese Academy of Sciences, Beijing. Her current research interests include robotic manipulation, brain-like robot and biologically inspired visual algorithms.