DocumentCode :
3172295
Title :
Application of structured total least squares for system identification
Author :
Markovsky, Ivan ; Willems, Jan C. ; Van Huffel, Sabine ; De Moor, Bart ; Pintelon, Rik
Author_Institution :
Electr. Eng. Dept., Katholieke Univ., Leuven, Heverlee, Belgium
Volume :
4
fYear :
2004
fDate :
14-17 Dec. 2004
Firstpage :
3382
Abstract :
The following identification problem is considered: minimize the ℓ2 norm of the difference between a given time series and an approximating one under the constraint that the approximating time series is a trajectory of a linear time invariant system of a fixed complexity. The complexity is measured by the input dimension and the maximum lag. The problem is known as the global total least squares and alternatively can be viewed as maximum likelihood identification in the errors-in-variables setup. Multiple time series and latent variables can be considered in the same setting. The identification problem is related to the structured total least squares problem. The paper presents an efficient software package that implements the theory in practice. The proposed method and software are tested on data sets from the database for the identification of systems DAISY.
Keywords :
computational complexity; identification; least squares approximations; mathematics computing; DAISY; errors-in-variables setup; global total least squares; identification problem; linear time invariant system; maximum likelihood identification; software package; structured total least squares problem; system identification; Databases; Kernel; Least squares approximation; Least squares methods; Signal processing; Software packages; Software testing; System identification; System testing; Time invariant systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2004. CDC. 43rd IEEE Conference on
ISSN :
0191-2216
Print_ISBN :
0-7803-8682-5
Type :
conf
DOI :
10.1109/CDC.2004.1429229
Filename :
1429229
Link To Document :
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