NDLI: Worst-case /spl Lscr//sub 1/ identification using mixed parametric-nonparametric models

Content Provider	IEEE Xplore Digital Library
Author	Elia, N. Milanese, M.
Copyright Year	1993
Description	Author affiliation: Dipartimento di Autom. e Inf., Politecnico di Torino, Italy (Elia, N.; Milanese, M.)
Abstract	In this paper we study the problem of identifying a system within a given class, using input-output measurements corrupted by /spl Lscr//sub /spl infin// bounded noise. The identification error is measured according to the /spl Lscr//sub 1/ norm of the impulse response, in the worst case with respect to allowable systems and noise. With nonparametric identification approach, the number of measurements needed to estimate n impulse response samples, within a given level of accuracy, grows exponentially with n, leading in most cases to unacceptable experimental conditions. In this paper we show that large improvements can be obtained with respect to the nonparametric approach by using more parsimonious classes of models, constituted of mixed parametric and nonparametric models.<>
Starting Page	545
Ending Page	550
File Size	553294
Page Count	6
File Format	PDF
ISBN	0780312988
DOI	10.1109/CDC.1993.325088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-12-15
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time varying systems Equations Parametric statistics Vectors Robust stability Ear Finite impulse response filter Control design Linear programming
Content Type	Text
Resource Type	Article

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