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Maximum likelihood LM identification based on particle filtering for scarce measurement-data MIMO Hammerstein Box-Jenkins systems

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  • Zong, Tiancheng
  • Li, Junhong
  • Lu, Guoping

Abstract

The scarce measurement-data system means that the input or output of one system are sampled at scarce time series. Thus, the sampled data are incomplete in scarce measurement-data systems. In this paper, the parameter estimation of scarce measurement-data multiple input multiple output Hammerstein Box-Jenkins (S-MIMO-H-BJ) systems is studied. To make full use of the system data without adding unknown parameters, the particle filtering method is applied to obtain unknown states and variables in scarce measurement-data systems. Thus, the maximum likelihood Levenberg Marquardt (ML-LM) iterative method based on particle filtering (ML-LM-I-PF) is derived. To verify the superiority of the proposed algorithm, the ML-LM iterative method based on auxiliary model (ML-LM-I-AM) is also derived. Finally, using these two algorithms, unknown parameters in the S-MIMO-H-BJ numerical example and the two-tank level system are identified. Simulations prove that these two methods can all estimate S-MIMO-H-BJ models effectively, but the ML-LM-I-PF method behaves better because it has smaller calculation amount and more accurate parameter estimation.

Suggested Citation

  • Zong, Tiancheng & Li, Junhong & Lu, Guoping, 2025. "Maximum likelihood LM identification based on particle filtering for scarce measurement-data MIMO Hammerstein Box-Jenkins systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 230(C), pages 241-255.
    • Handle: RePEc:eee:matcom:v:230:y:2025:i:c:p:241-255
    DOI: 10.1016/j.matcom.2024.11.012
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    References listed on IDEAS

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    1. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    2. Wang, Jixia & Xiao, Xiaofang & Li, Chao, 2023. "Least squares estimations for approximate fractional Vasicek model driven by a semimartingale," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 207-218.
    3. Luna, M. & Di Piazza, M.C. & La Tona, G. & Accetta, A. & Pucci, M., 2021. "Exploiting dynamic modeling, parameter identification, and power electronics to implement a non-dissipative Li-ion battery hardware emulator," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 48-65.
    4. Jing, Shaoxue, 2023. "Time-delay Hammerstein system identification using modified cross-correlation method and variable stacking length multi-error algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 288-300.
    5. Xu, Huan & Xu, Ling & Shen, Shaobo, 2024. "Online identification methods for a class of Hammerstein nonlinear systems using the adaptive particle filtering," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
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