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

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  • Huafeng Xia
  • Yongqing Yang
  • Feng Ding
  • Ahmed Alsaedi
  • Tasawar Hayat

Abstract

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.

Suggested Citation

  • Huafeng Xia & Yongqing Yang & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2019. "Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(6), pages 1121-1135, April.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:6:p:1121-1135
    DOI: 10.1080/00207721.2019.1590664
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    Cited by:

    1. Huafeng Xia & Feiyan Chen, 2020. "Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems," Mathematics, MDPI, vol. 8(12), pages 1-19, December.

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