DocumentCode
793817
Title
Power-spectrum identification in terms of rational models
Author
Tretter, S.A. ; Steiglitz, K.
Author_Institution
University of Maryland, College Park, MD, USA
Volume
12
Issue
2
fYear
1967
fDate
4/1/1967 12:00:00 AM
Firstpage
185
Lastpage
188
Abstract
A technique is described for the identification of unknown power-spectral densities from sampled data in terms of a rational function of
. The problem is reduced to the minimization of a function of
parameters, where
is the order of the numerator of the model. This criterion, called the "minimum residual" criterion, reduces to the maximum likelihood criterion when the observed signal is Gaussian. A computational technique is described for minimizing this function which uses filtering and correlation to obtain the gradient and an iterative descent method due to M. J. D. Powell for minimization. Some computational results are given in which the method is compared with all-pole and conventional spectrum estimation techniques.
. The problem is reduced to the minimization of a function of
parameters, where
is the order of the numerator of the model. This criterion, called the "minimum residual" criterion, reduces to the maximum likelihood criterion when the observed signal is Gaussian. A computational technique is described for minimizing this function which uses filtering and correlation to obtain the gradient and an iterative descent method due to M. J. D. Powell for minimization. Some computational results are given in which the method is compared with all-pole and conventional spectrum estimation techniques.Keywords
Spectral estimation; System identification; Adaptive control; Band pass filters; Frequency estimation; Matrix converters; Open loop systems; Optimal control; Parametric statistics; Process control; Regulators; Transfer functions;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.1967.1098544
Filename
1098544
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