Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique

For multivariable equation-error systems with an autoregressive moving average noise, this paper applies the decomposition technique to transform a multivariable model into several identification sub-models based on the number of the system outputs, and derives a data filtering and maximum likelihoo...

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Bibliographic Details
Published in:International journal of systems science 2019-04, Vol.50 (6), p.1121-1135
Main Authors: Xia, Huafeng, Yang, Yongqing, Ding, Feng, Alsaedi, Ahmed, Hayat, Tasawar
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive moving average
Computer simulation
data filtering
Economic models
Filtration
Least squares method
Mathematical models
maximum likelihood
Maximum likelihood estimation
multivariable system
Parameter estimation
Recursive methods
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Summary:For multivariable equation-error systems with an autoregressive moving average noise, this paper applies the decomposition technique to transform a multivariable model into several identification sub-models based on the number of the system outputs, and derives a data filtering and maximum likelihood-based recursive least-squares algorithm to reduce the computation complexity and improve the parameter estimation accuracy. A multivariable recursive generalised extended least-squares method and a filtering-based recursive extended least-squares method are presented to show the effectiveness of the proposed algorithm. The simulation results indicate that the proposed method is effective and can produce more accurate parameter estimates than the compared methods.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2019.1590664