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Iterative‐recursive estimation of parameters of regression models with resistance to outliers on practical examples
Abstract
Here, identification of processes and systems in the sense of the least sum of absolute values is taken into consideration. The respective absolute value estimators are recognised as exceptionally insensitive to large measurement faults or other defects in the processed data, whereas the classical least squares procedure appears to be completely impractical for processing the data contaminated with such parasitic distortions. Since the absolute value quality index cannot be minimised analytically, an iterative solution is used to find optimal estimates of the parameters of the underlying regression model. In addition, an approximate recursive estimator is proposed and implemented for on-line evaluation of system parameters. The convergence (basic property) of the iterative estimator is show to be proven and some aspects related to the absolute value criterion are explained. This allows for the formulation of practical conclusions and indication of directions for further research. In addition, the effectiveness of the described iterative-recursive estimation procedures is practically verified by appropriate numerical experiments.
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1049/cth2.12628
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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IET Control Theory and Applications
no. 18,
pages 1099 - 1113,
ISSN: 1751-8644 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Kozłowski J., Kowalczuk Z.: Iterative‐recursive estimation of parameters of regression models with resistance to outliers on practical examples// IET Control Theory and Applications -Vol. 18,iss. 9 (2024), s.1099-1113
- DOI:
- Digital Object Identifier (open in new tab) 10.1049/cth2.12628
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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