Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 737-747.doi: 10.23919/JSEE.2022.000027

• CONTROL THEORY AND APPLICATION • Previous Articles     Next Articles

Fuzzy identification of nonlinear dynamic system based on selection of important input variables

Jinfeng LYU1,2(), Fucai LIU1,*(), Yaxue REN1()   

  1. 1 Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao 066004, China
    2 School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
  • Received:2020-03-22 Online:2022-06-18 Published:2022-06-24
  • Contact: Fucai LIU;;
  • About author:|LYU Jinfeng was born in 1977. She is currently a Ph.D. candidate in Automation Department of Yanshan University and an associate professor in Hebei Normal University of Science and Technology. Her main research interests are identification and control of nonlinear systems. E-mail:||LIU Fucai was born in 1966. He received his master degree from the Automatic Control Department of Northeast Institute of Heavy Machinery in 1994, and his Ph.D. degree from the Control Science and Engineering Department of Harbin Institute of Technology in 2003. He is now the director of Automation Department of Yanshan University. His main research interests are nonlinear system fuzzy identification and predictive control, and space robot control technology. E-mail:||REN Yaxue was born in 1995. She received her bachelor degree in measurement and control technology and instrument from Hebei University, and master degree in control science and engineering from Yanshan University. She is currently a Ph.D. student in instrument science and technology at Yanshan University. Her main research interest is plane array capacitance detection technology. E-mail:
  • Supported by:
    This work was supported by the Natural Science Foundation of Hebei Province (F2019203505)


Input variables selection (IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure. Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indicate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno (T-S) fuzzy modeling.

Key words: Takagi-Sugeno (T-S) fuzzy modeling, input variable selection (IVS), fuzzy identification, fuzzy c-means clustering algorithm