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

Yingbo Hua, Tapan K. Sarkar

Research output: Contribution to journalConference article

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)
Pages (from-to)715-719
Number of pages5
JournalConference Record - Asilomar Conference on Circuits, Systems & Computers
Volume2
StatePublished - Dec 1 1988
Eventv 1 (of 2) - Pacific Grove, CA, USA
Duration: Oct 31 1988Nov 2 1988

ASJC Scopus subject areas

  • Engineering(all)

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