DocumentCode
2843740
Title
Comparisons of stochastic gradient and least squares algorithms for multivariable systems
Author
Liao, Yuwu ; Liu, Yanjun ; Feng Ding
Author_Institution
Dept. of Phys. & Electron. Inf. Technol., Xiangfan Univ., Xiangfan, China
fYear
2010
fDate
26-28 May 2010
Firstpage
3275
Lastpage
3279
Abstract
Two identification models are obtained for multivariable ARX systems by different parameterization, and the corresponding two least squares and two stochastic gradient algorithms are given based on the lest squares principle and the stochastic gradient search principle and minimizing different cost functions. The performances of these algorithms are analyzed and compared by the simulation tests.
Keywords
gradient methods; least mean squares methods; multivariable control systems; stochastic processes; least squares algorithms; multivariable ARX systems; parameter estimation; stochastic gradient algorithms; Algorithm design and analysis; Least squares methods; MIMO; Parameter estimation; Performance analysis; State estimation; State-space methods; Stochastic processes; Stochastic systems; Transfer functions; Least Squares; Multivariable Systems; Parameter Estimation; Recursive Identification; Stochastic Gradient;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location
Xuzhou
Print_ISBN
978-1-4244-5181-4
Electronic_ISBN
978-1-4244-5182-1
Type
conf
DOI
10.1109/CCDC.2010.5498589
Filename
5498589
Link To Document