DocumentCode
2236869
Title
Kernel density estimation in adaptive tracking
Author
Bercu, Bernard ; Portier, Bruno
Author_Institution
Inst. de Math. de Bordeaux, Univ. Bordeaux 1, Talence, France
fYear
2008
fDate
9-11 Dec. 2008
Firstpage
3441
Lastpage
3445
Abstract
We investigate the asymptotic properties of a recursive kernel density estimator associated with the driven noise of multivariate ARMAX models in adaptive tracking. We establish an almost sure pointwise and uniform strong law of large numbers as well as a pointwise and multivariate central limit theorem. We also carry out a goodness-of-fit test together with some simulation experiments.
Keywords
adaptive control; autoregressive moving average processes; recursive estimation; statistical testing; tracking; adaptive tracking control; driven noise; goodness-of-fit test; multivariate ARMAX model; multivariate central limit theorem; pointwise theorem; recursive kernel density estimation; Adaptive control; Chromium; Frequency; Kernel; Linear regression; Programmable control; Recursive estimation; Testing; Tin; Trajectory;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2008. CDC 2008. 47th IEEE Conference on
Conference_Location
Cancun
ISSN
0191-2216
Print_ISBN
978-1-4244-3123-6
Electronic_ISBN
0191-2216
Type
conf
DOI
10.1109/CDC.2008.4738648
Filename
4738648
Link To Document