Improving RELS local convergence via long-range predictive identifiers

Author

Chisci, L. ; Giarré, L. ; Mosca, E.

Author_Institution

Dipartimento di Sistemi e Inf., Firenze Univ., Italy

fYear

1992

fDate

1992

Firstpage

527

Abstract

A recursive autoregressive moving-average (ARMA) identifier based on multistep prediction error minimization is considered in order to sidestep the positive realness conditions of recursive extended least squares (RELS). For a single-step prediction horizon, the algorithm reduces to RELS while, as shown by local convergence analysis, it tends to be a local approximation of the recursive maximum likelihood (RML) algorithm as the prediction horizon increases

Keywords

adaptive control; convergence of numerical methods; identification; predictive control; RELS local convergence; local approximation; long-range predictive identifiers; multistep prediction error minimization; recursive autoregressive moving-average; recursive extended least squares; recursive maximum likelihood; single-step prediction horizon; Algorithm design and analysis; Approximation algorithms; Convergence; Least squares approximation; Least squares methods; Maximum likelihood estimation; Parameter estimation; Polynomials; Prediction algorithms; Recursive estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-0872-7

Type

conf

DOI

10.1109/CDC.1992.371678

Filename

371678

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2416422