NDLI: Approximate system identification with missing data

Content Provider	IEEE Xplore Digital Library
Author	Markovsky, I.
Copyright Year	2013
Description	Author affiliation: Dept. ELEC, Vrije Univ. Brussel, Brussels, Belgium (Markovsky, I.)
Abstract	Linear time-invariant system identification is considered in the behavioral setting. Nonstandard features of the problem are specification of missing and exact variables and identification from multiple time series with different length. The problem is equivalent to mosaic Hankel structured low-rank approximation with element-wise weighted cost function. Zero/infinite weights are assigned to the missing/exact data points. The problem is in general nonconvex. A solution method based on local optimization is outlined and compared with alternative methods on simulation examples. In a stochastic setting, the problem corresponds to errors-invariables identification. A modification of the generic problem considered is presented that is a deterministic equivalent to the classical ARMAX identification. The modification is also a mosaic Hankel structured low-rank approximation problem.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	156
Ending Page	161
File Size	141671
Page Count	6
File Format	PDF
ISBN	9781467357142
ISSN	07431546
e-ISBN	9781467357173
DOI	10.1109/CDC.2013.6759875
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Minimization Complexity theory Computational modeling Least squares approximations Noise level low-rank approximation system identification behavioral approach missing data mosaic Hankel matrix
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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