Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (4): 761-769.doi: 10.23919/JSEE.2020.000051

• Systems Engineering • Previous Articles     Next Articles

Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method

Hongwei WANG*(), Penglong FENG()   

  1. 1 School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
    2 School of Control Science and Engineering, Dalian University of Technology, Dalian 110024, China
  • Received:2019-08-19 Online:2020-08-25 Published:2020-08-25
  • Contact: Hongwei WANG;
  • About author:WANG Hongwei was born in 1969. He received hisPh.D. degree in aircraft simulation and control from HarbinTechnology of Institute in 1999. He is a professor with DalianUniversity of Technology. He is currently working at XinjiangUniversity to help the university in discipline construction. Hisresearch interests include switched system identification, nonlinearsystem identification, fuzzy mo deling, and control of nonlinearsystems. E-mail:|FENG Penglong was born in 1994. He received his B.S. degree in automation from Xinjiang University in 2016. He is now pursuing his M.A. degree at the School of Electrical Engineering, Xinjiang University. His research interests are system identification and fuzzy modeling. E-mail:
  • Supported by:
    the National Natural Science Foundation of China(61863034);This work was supported by the National Natural Science Foundation of China (61863034)


Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied. Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

Key words: multirate sampled data, nonlinear system, fuzzy model, multi-model