Parameter estimation algorithms for missing-data systems

Author

Ding, Jie ; Jie Ding

Author_Institution

Sch. of Commun. & Control Eng., Jiangnan Univ., Wuxi, China

fYear

2009

fDate

10-12 June 2009

Firstpage

5032

Lastpage

5036

Abstract

This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary (reference) model approach, we present a recursive least squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the stochastic framework. The basic idea is to replace the unmeasurable inner variables with the output of an auxiliary model.

Keywords

convergence of numerical methods; least squares approximations; recursive estimation; stochastic processes; convergence properties; missing output estimation; missing-data systems; output error models; output estimation; parameter estimation algorithms; parameter identification; recursive least squares algorithm; stochastic framework; Communication system control; Control systems; Convergence; Least squares approximation; Parameter estimation; Predictive models; Recursive estimation; Sampling methods; Stochastic systems; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2009. ACC '09.

Conference_Location

St. Louis, MO

ISSN

0743-1619

Print_ISBN

978-1-4244-4523-3

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2009.5159806

Filename

5159806