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Nuclear norm minimization methods for frequency domain subspace identification

Author(s):

R. S. Smith
Conference/Journal:

American Control Conference, Montréal, Canada, pp. 2689-2694
Abstract:

Frequency domain subspace identification is an effective means of obtaining a low order model from frequency domain data. In the noisy data case using a singular value decomposition to determine the observable subspace has several problems: an incorrect weighting of the data in the singular values; difficulties in determining the appropriate rank; and a loss of the Hankel structure in the low order approximation. A nuclear norm (sum of the singular values) minimization based method, using spectral constraints, is presented here and shown to be an effective technique for overcoming these problems.

Year:

2012
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { Xxx:2012:IFA_3913,
    author={R. S. Smith},
    title={{Nuclear norm minimization methods for frequency domain
	  subspace identification}},
    booktitle={American Control Conference},
    pages={2689--2694},
    year={2012},
    address={Montr{\'e}al, Canada},
    month=jun,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3913}
}
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