Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

doi:10.1007/s40436-022-00426-w

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (4): 694-707.doi: 10.1007/s40436-022-00426-w

• • 上一篇

Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

Feng Li¹, Li Jia², Ya Gu³

1. College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, People's Republic of China;
2. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200444, People's Republic of China;
3. School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, Jiangsu, People's Republic of China

收稿日期:2021-08-15 修回日期:2022-03-24 发布日期:2023-10-27
通讯作者: Feng Li,E-mail:lifeng@jsut.edu.cn E-mail:lifeng@jsut.edu.cn
作者简介:Feng Li received the B.S. degree in electrical engineering and automation from Yangzhou University, China in 2011 and the M.S. degree in control science and engineering from Yangzhou University, China in 2014, and the Ph.D. degree in Control Theory and Control Engineering from Shanghai University in 2018. Now he is an associate professor in College of Electrical and Information Engineering, Jiangsu University of Technology. His research work is in the areas of neural networks, fuzzy control and learning algorithm based on data driven, which are towards the development of innovative identifcation and control strategies for complex process systems.
Li Jia received the PhD degree in Control Theory and Control Engineering from East China University of Science & Technology, China in 2003. Then she worked as a Research fellow in National University of Singapore from March 2003 to June 2005. Now she is a professor in School of Mechatronics Engineering and Automation, Shanghai University. Her research work is in the areas of fuzzy control, neural networks and intelligent tools, which are towards the development of innovative identifcation and control strategies for complex process systems.
Ya Gu received the Ph.D. degree in automatic control from Jiangnan University, Wuxi, China in 2015. She is now a Lecturer in Changshu Institute of Technology, Suzhou, China, and has been a visiting Ph.D. student from 2014 to 2015 at the University of Alberta, Edmonton, AB, Canada. Her current research interests include model identifcation and adaptive control.
基金资助:
This research is supported by the National Natural Science Foundation of China (Grant No. 62003151), the Natural Science Foundation of Jiangsu Province (Grant No. BK20191035), the Changzhou Sci&Tech Program (Grant No. CJ20220065), the “Blue Project” of Universities in Jiangsu Province, and Zhongwu Youth Innovative Talents Support Program in Jiangsu Institute of Technology.

Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

Feng Li¹, Li Jia², Ya Gu³

1. College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, People's Republic of China;
2. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200444, People's Republic of China;
3. School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, Jiangsu, People's Republic of China

Received:2021-08-15 Revised:2022-03-24 Published:2023-10-27
Contact: Feng Li,E-mail:lifeng@jsut.edu.cn E-mail:lifeng@jsut.edu.cn
Supported by:
This research is supported by the National Natural Science Foundation of China (Grant No. 62003151), the Natural Science Foundation of Jiangsu Province (Grant No. BK20191035), the Changzhou Sci&Tech Program (Grant No. CJ20220065), the “Blue Project” of Universities in Jiangsu Province, and Zhongwu Youth Innovative Talents Support Program in Jiangsu Institute of Technology.

摘要/Abstract

摘要： In this study, a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented. The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process. The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals, thus the interference of process disturbance is solved. Furthermore, the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals, and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model. Finally, convergence analysis of the suggested identification scheme is derived using stochastic process theory. The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00426-w

关键词: Nonlinear process, Parameter identification, Hammerstein-Wiener model, Neural fuzzy model, Multiple signal processing

Abstract: In this study, a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented. The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process. The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals, thus the interference of process disturbance is solved. Furthermore, the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals, and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model. Finally, convergence analysis of the suggested identification scheme is derived using stochastic process theory. The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00426-w

Key words: Nonlinear process, Parameter identification, Hammerstein-Wiener model, Neural fuzzy model, Multiple signal processing

Feng Li, Li Jia, Ya Gu. Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing[J]. Advances in Manufacturing, 2023, 11(4): 694-707.

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Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

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