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4SID Linear Regression
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1927-1690
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
1994 (English)In: Proc. IEEE 33rd Conf.on Decision and Control, IEEE conference proceedings, 1994, 2858-2863 p.Conference paper (Refereed)
Abstract [en]

State-space subspace system identification (4SID) has been suggested as an alternative to more traditional prediction error system identification, such as ARX least squares estimation. The aim of this note is to analyse the connections between these two different approaches to system identification. The conclusion is that 4SID can be viewed as a linear regression multistep ahead prediction error method, with certain rank constraints. This allows us to analyse 4SID methods within the standard framework of system identification and linear regression estimation. For example, it is shown that ARX models have nice properties in terms of 4SID identification. From a linear regression model, estimates of the extended observability matrix are found. Results from an asymptotic analysis are presented, i.e. explicit formulas for the asymptotic variances of the pole estimation error are given. From these expressions, some difficulties in choosing user specified parameters are pointed out in an example.

Place, publisher, year, edition, pages
IEEE conference proceedings, 1994. 2858-2863 p.
Keyword [en]
Additive noise, Automatic control, Iterative algorithms, Linear regression, Noise measurement, Observability, Signal processing, Space technology, State-space methods, System identification
National Category
Control Engineering Signal Processing
URN: urn:nbn:se:kth:diva-86559DOI: 10.1109/CDC.1994.411364ISBN: 0-7803-1968-0OAI: diva2:500840
IEEE 33rd Conf. on Decision and Control, Orlando, FL, USA, Dec. 1994

QC 20140805

Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2015-02-02Bibliographically approved

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