DocumentCode
3426969
Title
Random sequences optimal estimation by using regression and wavelets
Author
Amosov, Oleg S. ; Amosova, Liudmila N.
Author_Institution
Amur State Univ. of Humanities & Pedagogy, Komsomolsk-on-Amur, Russia
fYear
2009
fDate
9-11 Dec. 2009
Firstpage
2293
Lastpage
2298
Abstract
This paper is concerned with random sequences optimal estimation by using regression with unknown type of the regression function and wavelets. The regression and wavelet based estimation algorithms are offered. It is shown that the Bayesian and the alternative regression and wavelet based algorithms provide estimates with the close accuracy. Two examples for linear and nonlinear filtering and prediction problems are considered.
Keywords
Bayes methods; nonlinear filters; prediction theory; random sequences; regression analysis; wavelet transforms; Bayesian; nonlinear filtering; prediction problem; random sequence optimal estimation; regression function; wavelet based estimation; Automatic control; Bayesian methods; Filtering; Filters; Fuzzy systems; Neural networks; Random sequences; Signal processing algorithms; Smoothing methods; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Automation, 2009. ICCA 2009. IEEE International Conference on
Conference_Location
Christchurch
Print_ISBN
978-1-4244-4706-0
Electronic_ISBN
978-1-4244-4707-7
Type
conf
DOI
10.1109/ICCA.2009.5410327
Filename
5410327
Link To Document