Abstract
In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously. However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application. In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented. The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated. The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms. The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.
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This work was supported by National Natural Science Foundation of China (No. 61171145).
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Jian-Guo Wang received the Ph.D. degree in control science and engineering from Shanghai Jiao Tong University, China in 2007. Now he is a faculty member in the Department of Automation, Shanghai University, China. His research interests include the statistical modeling and control of multivariate process, performance monitoring and energy saving optimization of power system, steel rolling and heating furnace.
Qian-Ping Xiao received the B. Sc. degree in electronic science and technology from Jingdezhen Ceramic Institute, China in 2012, and now is a master student in pattern recognition and intelligent system at Shanghai University, China. His research interests include subspace identification and performance evaluation of model predictive control systems.
Tiao Shen received the B. Sc. degree in electrical engineering and automation from Shanghai University, China in 2014, and now is a master student in pattern recognition and intelligent system at Shanghai University, China. His research interests include machine learning and data mining.
Shi-Wei Ma received the B. Sc. and the M. Sc. degrees in electronics from Lanzhou University, China in 1986 and 1991, respectively, and received the Ph.D. degree in control theory and engineering from Shanghai University, China in 2000. From 2001 to 2003, he was a Japan science and technology research fellow at the National Institute of Industrial Safety of Japan. From 2003 to 2008, he was an associate professor, and since 2008, he has been a professor, in the Department of Automation at Shanghai University, China. His research interests include signal processing, pattern recognition and intelligent system.
Wen-Tao Rao received the B. Sc. and M. Sc. degrees in thermal energy power engineering from Kunming University of Science and Technology, China in 1990 and 1993, respectively, and received the Ph.D. degree in thermal energy power engineering from Tongji University in 2002. He is a postdoctoral of Kunming University of Science and Technology in 2006. His research interests include furnaces technology and control technology, energy saving technology and equipment, new energy, energy management contract.
Yong-Jie Zhang received the B. Sc. degree in non-ferrous metallurgy from Northeastern University, China in 1992, and received the M. Sc. and Ph.D. degrees in thermal power engineering from Northeastern University in 1992 and 2005, respectively. His research interests include the basic study of new generation of steel materials and the development of electromagnetic continuous casting technology and equipment.
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Wang, JG., Xiao, QP., Shen, T. et al. Simultaneous Identification of Process Structure, Parameters and Time-Delay Based on Non-negative Garrote. Int. J. Autom. Comput. 17, 873–882 (2020). https://doi.org/10.1007/s11633-015-0948-0
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DOI: https://doi.org/10.1007/s11633-015-0948-0