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System modeling based on orthotopic expansion particle filter for linear time-varying system with bounded noise

Author

Listed:
  • Wang, Ziyun
  • Feng, Chao
  • Wang, Yan
  • Wang, Zhenhua

Abstract

To make parameter estimation more accurate and concise, a parameter identification method based on particle evaluation and orthotope is presented in this paper. First, all possible expansion directions of the parameter-feasible domain (predicted orthotope) were evaluated. Further particle evaluation was carried out in the expanded orthotope to shrink the feasible domain. Once the upper and lower bounds of the parameter estimation were obtained, a particle filter with improved sampling was used for state estimation. Finally, the effectiveness and accuracy of the proposed algorithm were verified through simulations.

Suggested Citation

  • Wang, Ziyun & Feng, Chao & Wang, Yan & Wang, Zhenhua, 2026. "System modeling based on orthotopic expansion particle filter for linear time-varying system with bounded noise," Applied Mathematics and Computation, Elsevier, vol. 514(C).
    • Handle: RePEc:eee:apmaco:v:514:y:2026:i:c:s0096300325005417
    DOI: 10.1016/j.amc.2025.129816
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    References listed on IDEAS

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    1. Xie, Haitian, 2023. "Uniform convergence results for the local linear regression estimation of the conditional distribution," Statistics & Probability Letters, Elsevier, vol. 201(C).
    2. Mi, Wen & Qian, Tao, 2022. "System identification of hammerstein models by using backward shift algorithm," Applied Mathematics and Computation, Elsevier, vol. 413(C).
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    6. Haitian Xie, 2021. "Uniform Convergence Results for the Local Linear Regression Estimation of the Conditional Distribution," Papers 2112.08546, arXiv.org, revised Jun 2023.
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