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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/31204

Title
A stochastic approach to estimation in H∞
Author/Creator
Ninness, Brett
Description
This paper examines the problem of system identification from frequency response data. Recent approaches to this problem, known collectively as "Estimation in H∞", involve deterministic descriptions of noise corruptions to the data. In order to provide "worst-case" convergence with respect to these deterministic noise descriptions, non-linear in the data algorithms are required. In contrast, this paper examines "worst-case" estimation in H infinity when the disturbances are subject to mild stochastic assumptions and linear in the data algorithms are employed. Issues of convergence, error bounds, and model order selection are considered.
Relation
Automatica Vol. 34, Issue 1, p. 405-414
Publisher Link
http://dx.doi.org/10.1016/S0005-1098(97)00219-7
Date
1998
Publisher
Elsevier
Keyword(s)
estimation theory;  algorithms;  system identification;  robust estimation;  error analysis
Resource Type
journal article
Identifier
http://hdl.handle.net/1959.13/31204
Identifier
ISSN:0005-1098
Language
eng
Reviewed
Reviewed
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Subject
"Automatica"
 
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