Title :
Parameter estimation algorithms for missing-data systems
Author :
Ding, Jie ; Jie Ding
Author_Institution :
Sch. of Commun. & Control Eng., Jiangnan Univ., Wuxi, China
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;
Conference_Titel :
American Control Conference, 2009. ACC '09.
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-4523-3
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2009.5159806
