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Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation

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  • Ping Ma
  • Feng Ding
  • Quanmin Zhu

Abstract

This paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the parameter accuracy, a decomposition based multi-innovation recursive generalised least squares algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective.

Suggested Citation

  • Ping Ma & Feng Ding & Quanmin Zhu, 2018. "Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(5), pages 920-928, April.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:5:p:920-928
    DOI: 10.1080/00207721.2018.1433247
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