DocumentCode
518015
Title
Notice of Retraction
Convergence of the auxiliary model based stochastic gradient algorithm for multiple-input single-output systems
Author
Yuwu Liao ; Yanjun Liu ; Rui Feng Ding
Author_Institution
Dept. of Phys. & Electron., Xiangfan Univ., Xiangfan, China
Volume
4
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper studies the convergence of the auxiliary model based stochastic gradient parameter estimation algorithm for multi-input output-error systems by using the martingale convergence theorem. The basic idea is to formulate a positive definite function of the parameter estimation error and to indicate that the parameter estimates converge to their true values under persistent excitation. The proposed algorithm has less computational burden that existing least squares identification algorithms. A simulation example is given.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper studies the convergence of the auxiliary model based stochastic gradient parameter estimation algorithm for multi-input output-error systems by using the martingale convergence theorem. The basic idea is to formulate a positive definite function of the parameter estimation error and to indicate that the parameter estimates converge to their true values under persistent excitation. The proposed algorithm has less computational burden that existing least squares identification algorithms. A simulation example is given.
Keywords
MIMO systems; convergence; gradient methods; least squares approximations; parameter estimation; stochastic processes; stochastic systems; auxiliary model; least squares identification algorithms; martingale convergence theorem; multi-input output-error systems; multiple-input single-output systems; stochastic gradient parameter estimation algorithm; Bismuth; Computer errors; Convergence; Delay estimation; Information technology; Least squares methods; Parameter estimation; Physics; Polynomials; Stochastic systems; auxiliary model; convergence properties; martingale convergence theorem; recursive identification; stochastic gradient;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485437
Filename
5485437
Link To Document