1. Robust identification of linear ARX models with recursive EM algorithm based on Student's t-distribution.
- Author
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Chen, Xin, Zhao, Shunyi, and Liu, Fei
- Subjects
- *
EXPECTATION-maximization algorithms , *AUTOREGRESSIVE models , *DEGREES of freedom , *ALGORITHMS , *LINEAR systems , *IDENTIFICATION - Abstract
• 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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