DocumentCode
619948
Title
Modified recursive extended least squares identification algorithms
Author
Ai-guo Wu ; Zhi-Guang Wang
Author_Institution
Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China
fYear
2013
fDate
25-27 May 2013
Firstpage
1562
Lastpage
1567
Abstract
For ARMAX models, modified recursive extended least squares identification algorithms are presented. The basic idea lies in two aspects. One is to decompose the original system into two subsystems. The other is that the most recent information is used to update the parameters, which is different from the hierarchical principle. A simulation example is employed to test the effectiveness of the proposed algorithms.
Keywords
autoregressive moving average processes; least squares approximations; ARMAX model; autoregressive moving average with exogenous input model; hierarchical principle; recursive extended least squares identification algorithm; Algorithm design and analysis; Autoregressive processes; Convergence; Indexes; Noise; Prediction algorithms; Vectors; ARMAX; Extended least squares identification; Hierarchical principle;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location
Guiyang
Print_ISBN
978-1-4673-5533-9
Type
conf
DOI
10.1109/CCDC.2013.6561177
Filename
6561177
Link To Document