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Regularized Least Squares Recursive Algorithm with Forgetting Factor for Identifying Parameters in the Grinding Process

Author

Listed:
  • Yang Yu
  • Rui Deng
  • Gang Yu
  • Yu Wang
  • Guodong Yang
  • DaYong Zhao

Abstract

This paper investigates a parameter identification problem in the grinding process. Due to the data saturated phenomenon and the ill‐posed of parameter identification inverse problem, this paper presents a regularized least squares recursive algorithm with a forgetting factor (RLSRAFF), the basic idea of which is to combine the forgetting factor with regularization parameters. Moreover, based on RLSRAFF, this paper verifies the recursive calculation of criterion function, analyzes the effect of calculation error from the gain matrix and proves the convergence of the proposed algorithm. Finally, effectiveness of RLSRAFF is verified by simulation experiments and grinding data. Compared with other algorithms, RLSRAFF can give a more convergence rate to the real data and reduce the error from the true value.

Suggested Citation

  • Yang Yu & Rui Deng & Gang Yu & Yu Wang & Guodong Yang & DaYong Zhao, 2022. "Regularized Least Squares Recursive Algorithm with Forgetting Factor for Identifying Parameters in the Grinding Process," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:5188389
    DOI: 10.1155/2022/5188389
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    References listed on IDEAS

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    1. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.
    2. Xiangdong Sun & Jingrun Ji & Biying Ren & Chenxue Xie & Dan Yan, 2019. "Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery," Energies, MDPI, vol. 12(12), pages 1-15, June.
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