Deterministic convergence analysis of RLS estimators with different forgetting factors

Author

Bittanti, S. ; Bolzern, P. ; Campi, M. ; Coletti, E.

Author_Institution

Dipartimento di Elettronica, Politecnico di Milano, Italy

fYear

1988

fDate

7-9 Dec 1988

Firstpage

1530

Abstract

Three forgetting factors recursive-least-squares (RLS) algorithms, as well as the classical error-forgetting one, are considered. The basic assumptions are that the data-generation mechanism is deterministic, the unknown parameter vector is constant, and the observation vector is persistently exciting. It is possible to prove the convergence of the estimates supplied by the various algorithms to the true parameter vector. This conclusion does not mean that the algorithm possess tracking capabilities when the unknown parameter vector is time-varying. In this case, the exponential convergence in the case of constant unknown parameters is much more important

Keywords

convergence of numerical methods; least squares approximations; parameter estimation; constant parameters; data-generation mechanism; deterministic convergence analysis; error forgetting algorithm; exponential convergence; parameter estimation forgetting factors RLS algorithms; persistently exciting observation vector; recursive least-squares algorithms; Adaptive control; Algorithm design and analysis; Convergence; Delay; Equations; Least squares approximation; Parameter estimation; Recursive estimation; Regression analysis; Resonance light scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1988., Proceedings of the 27th IEEE Conference on

Conference_Location

Austin, TX

Type

conf

DOI

10.1109/CDC.1988.194583

Filename

194583