Bounded error identification of time-varying parameters by RLS techniques

Author

Bittanti, Sergio ; Campi, Marco

Author_Institution

Dipartimento di Elettronica, Politecnico di Milano, Italy

Volume

39

Issue

5

fYear

1994

fDate

5/1/1994 12:00:00 AM

Firstpage

1106

Lastpage

1110

Abstract

The performance of the recursive least squares algorithm with constant forgetting factor in the identification of time-varying parameters is studied in a stochastic framework. It is shown that the mean square tracking error keeps bounded if and only if the so-called covariance matrix of the algorithm is L¹-bounded. Then, a feasibility range for the forgetting factor is worked out in correspondence of which the covariance matrix (and therefore the tracking error) keeps bounded

Keywords

identification; least squares approximations; matrix algebra; parameter estimation; tracking; L¹-bounded; RLS techniques; bounded error identification; constant forgetting factor; covariance matrix; feasibility range; mean square tracking error; recursive least squares algorithm; stochastic framework; time-varying parameters; tracking error; Covariance matrix; Equations; Filtering algorithms; Least squares methods; Mathematical model; Resonance light scattering; Signal processing algorithms; Stochastic processes; Time measurement; Time varying systems;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.284904

Filename

284904