Proportional–integral-type estimator design for delayed recurrent neural networks under encoding–decoding mechanism

Abstract

In this paper, the proportional–integral-type estimator design problem is studied for recurrent neural networks under the encoding–decoding communication mechanism. In the process of the measurement data transmission, an encoding–decoding mechanism is introduced to improve the security of the network by encrypting the measurement data. The purpose of this paper is to design a proportional–integral-type estimation algorithm such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, a sufficient condition is obtained for the existence of the desired estimator. Then, the parameters of the estimator are obtained by solving certain matrix inequality. Finally, a simulation example is given to verify the effectiveness of the designed estimation algorithm.

Keywords:

• Recurrent neural networks
• proportional–integral-type estimator
• encoding-decoding mechanism
• uniform quantiser

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

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

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants U21A2019, 61933007, 61873058, and 62103095, the Natural Science Foundation of Heilongjiang Province of China under Grant LH2021F005, the Hainan Province Science and Technology Special Fund under Grant ZDYF2022SHFZ105, the Fundamental Research Funds for Provincial Undergraduate Universities of Heilongjiang Province of China under Grant 2018YDL-01, the Guiding Science and Technology Plan Project of Daqing of China under Grant zd-2019-06 and the Alexander von Humboldt Foundation of Germany.

Notes on contributors

Fan Yang

Fan Yang received the M. Eng. degree in control science and engineering from Northeast Petroleum University, Daqing, China, in 2017. She is currently an lecturer with the Artificial Intelligence Energy Research Institute, Northeast Petroleum University. Her current research interests include networked control systems, and oil gas information and control engineering.

Jiahui Li

Jiahui Li received the Ph.D. degree in petroleum and natural gas engineering at the Northeast Petroleum University, Daqing, China. From 2018 to 2019, she was a Visiting Scholar with the Department of Computer Science, Brunel University London, London, U.K. She is currently an Associate Professor with the Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China. Her current research interests include networked control systems, and oil gas information and control engineering.

Hongli Dong

Hongli Dong received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2012. From 2009 to 2010, she was a Research Assistant with the Department of Applied Mathematics, City University of Hong Kong, Hong Kong. From 2010 to 2011, she was a Research Assistant with the Department of Mechanical Engineering, The University of Hong Kong, Hong Kong. From 2011 to 2012, she was a Visiting Scholar with the Department of Information Systems and Computing, Brunel University London, London, U.K. From 2012 to 2014, she was an Alexander von Humboldt Research Fellow with the University of Duisburg-Essen, Duisburg, Germany. She is currently a Professor with the Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China. She is also the Director of the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing. Her current research interests include robust control and networked control systems. Dr. Dong is a very active reviewer for many international journals.

Yuxuan Shen

Yuxuan Shen received the Ph.D. degree in control science and engineering from the Donghua University, Shanghai, China, in 2020. From June 2018 to September 2018, he was a Research Assistant in the Texas A&M University at Qatar, Doha, Qatar. From November 2018 to November 2019, 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 Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China. Dr. Shen is a very active reviewer for many international journals.