Robust identification of linear ARX models with recursive EM algorithm based on Student’s t-distribution
•The outliers in the measurements are coped with the Student’s t-distribution, which assigns robustness to the algorithm according to its property of heavy-tails.•The robust identification issue is solved under recursive expectation-maximization algorithm. The online updating of the parameters are r...
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| Vydáno v: | Journal of the Franklin Institute Ročník 358; číslo 1; s. 1103 - 1121 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elmsford
Elsevier Ltd
01.01.2021
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0016-0032, 1879-2693, 0016-0032 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •The outliers in the measurements are coped with the Student’s t-distribution, which assigns robustness to the algorithm according to its property of heavy-tails.•The robust identification issue is solved under recursive expectation-maximization algorithm. The online updating of the parameters are realized based on a recursive Q-function.•The degree of freedom of the Student’s t-distribution is online updated with the implementation of a recursive auxiliary quantity.
This paper considers the robust identification issue of linear systems represented by autoregressive exogenous models using the recursive expectation-maximization (EM) algorithm. In this paper, a recursive Q-function is formulated based on the maximum likelihood principle. Meanwhile, the outliers that frequently appear in practical processes are accommodated with the Student’s t-distribution. The parameter vector, variance of noise, and the degree of freedom are recursively estimated. Finally, a numerical example, as well as a simulated continuous stirred tank reactor (CSTR) system, is performed to verify the effectiveness of the proposed recursive EM algorithm. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0016-0032 1879-2693 0016-0032 |
| DOI: | 10.1016/j.jfranklin.2020.06.003 |