The filtering based maximum likelihood recursive least squares estimation for multiple-input single-output systems

Feiyan Chen, Feng Ding*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


In this paper, we use a noise transfer function to filter the input-output data and propose a new recursive algorithm for multiple-input single-output systems under the maximum likelihood principle. The main contributions of this paper are to derive a filtering based maximum likelihood recursive least squares (F-ML-RLS) algorithm for reducing computational burden and to present two recursive least squares algorithms to show the effectiveness of the F-ML-RLS algorithm. In the end, an illustrative simulation example is provided to test the proposed algorithms and we show that the F-ML-RLS algorithm has a high computational efficiency with smaller sizes of its covariance matrices and can produce more accurate parameter estimates.

Original languageEnglish
Pages (from-to)2106-2118
Number of pages13
JournalApplied Mathematical Modelling
Issue number3
Publication statusPublished - 1 Feb 2016
Externally publishedYes


  • Data filtering
  • Least squares
  • Maximum likelihood
  • Multiple-input systems
  • System identification

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