Existence of stationary points for pseudo-linear regression identification algorithms

The authors prove the existence of a stable transfer function satisfying the nonlinear equations characterizing an asymptotic stationary point, in undermodeled cases, for a class of pseudo-linear regression algorithms, including Landau's algorithm, the Feintuch algorithm, and (S)HARF. The proof...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 44; no. 5; pp. 994 - 998
Main Authors: Regalia, P.A., Mboup, M., Ashari, M.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.1999
Institute of Electrical and Electronics Engineers
Subjects:
Algorithms
Applied sciences
Asymptotic properties
Automatic control
Computer science; control theory; systems
Control theory. Systems
Convergence
Density
Density functional theory
Exact sciences and technology
Filters
Frequency
Joints
Modelling and identification
Nonlinear equations
Reduced order systems
Regression
Spectra
Stability analysis
Stochastic processes
System identification
Transfer functions
Regression
Stationary process
System identification
Transfer function
Scale model
Adaptive method
ISSN:0018-9286
Online Access:Get full text
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Summary:The authors prove the existence of a stable transfer function satisfying the nonlinear equations characterizing an asymptotic stationary point, in undermodeled cases, for a class of pseudo-linear regression algorithms, including Landau's algorithm, the Feintuch algorithm, and (S)HARF. The proof applies to all degrees of undermodeling and assumes only that the input power spectral density function is bounded and nonzero for all frequencies, and that the compensation filter is strictly minimum phase. Some connections to previous stability analyses for reduced-order identification in this algorithm class are brought out.
Bibliography:ObjectType-Article-2
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ISSN:0018-9286
DOI:10.1109/9.763215