Maximum likelihood, weighted Kalman and subspace linear prediction algorithms for system identification

Yingbo Hua, Tapan Kumar Sarkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

For the problem of estimating parameters of a linear system from its input and output sequences, the authors present iterative quadratic maximum-likelihood (IQML), iterative quadratic weighted Kalman (IQWK), and noniterative subspace linear prediction (SLP) algorithms. The presence of forced response, free response, and noise is taken into account. The IQML algorithm is only for the case where either the free response or the forced response is absent from the output. The IQWK algorithm is provided as another classical method for the system identification problem. The SLP algorithms are based on a novel subspace deconvolution of the output. In particular, a double total-least-squares SLP algorithm is provided.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Circuits, Systems & Computers
EditorsRay R. Chen
PublisherPubl by Maple Press, Inc
Pages715-719
Number of pages5
Volume2
StatePublished - 1988
Eventv 1 (of 2) - Pacific Grove, CA, USA
Duration: Oct 31 1988Nov 2 1988

Other

Otherv 1 (of 2)
CityPacific Grove, CA, USA
Period10/31/8811/2/88

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Hua, Y., & Sarkar, T. K. (1988). Maximum likelihood, weighted Kalman and subspace linear prediction algorithms for system identification. In R. R. Chen (Ed.), Conference Record - Asilomar Conference on Circuits, Systems & Computers (Vol. 2, pp. 715-719). Publ by Maple Press, Inc.