The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La méthode d'identification récursive robuste des modèles ARX est proposée en utilisant la divergence bêta. La loi de mise à jour des paramètres proposée supprime l'effet des valeurs aberrantes à l'aide d'une fonction de pondération automatiquement déterminée en minimisant la divergence bêta. Un exemple numérique illustre l’efficacité de la méthode proposée.
Shuichi FUKUNAGA
Tokyo Metropolitan College of Industrial Technology
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Shuichi FUKUNAGA, "Robust Recursive Identification of ARX Models Using Beta Divergence" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 12, pp. 1580-1584, December 2023, doi: 10.1587/transfun.2023EAL2011.
Abstract: The robust recursive identification method of ARX models is proposed using the beta divergence. The proposed parameter update law suppresses the effect of outliers using a weight function that is automatically determined by minimizing the beta divergence. A numerical example illustrates the efficacy of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023EAL2011/_p
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@ARTICLE{e106-a_12_1580,
author={Shuichi FUKUNAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Robust Recursive Identification of ARX Models Using Beta Divergence},
year={2023},
volume={E106-A},
number={12},
pages={1580-1584},
abstract={The robust recursive identification method of ARX models is proposed using the beta divergence. The proposed parameter update law suppresses the effect of outliers using a weight function that is automatically determined by minimizing the beta divergence. A numerical example illustrates the efficacy of the proposed method.},
keywords={},
doi={10.1587/transfun.2023EAL2011},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Robust Recursive Identification of ARX Models Using Beta Divergence
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1580
EP - 1584
AU - Shuichi FUKUNAGA
PY - 2023
DO - 10.1587/transfun.2023EAL2011
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2023
AB - The robust recursive identification method of ARX models is proposed using the beta divergence. The proposed parameter update law suppresses the effect of outliers using a weight function that is automatically determined by minimizing the beta divergence. A numerical example illustrates the efficacy of the proposed method.
ER -