Coupled-least-squares identification for multivariable systems

This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is...

Full description

Saved in:
Bibliographic Details
Published in:IET control theory & applications Vol. 7; no. 1; pp. 68 - 79
Main Author: Ding, Feng
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 01.01.2013
John Wiley & Sons, Inc
Subjects:
Algorithms
Convergence
convergence theorems
coupled‐least‐squares parameter identification algorithm
C‐LS algorithm
identification problems
Inversions
Joining
least squares approximations
Least squares method
Mathematical models
MIMO systems
multiinput multioutput systems
multiple linear regression models
Multivariable
multivariable recursive least‐squares algorithm
multivariable RLS algorithm
multivariable systems
parameter estimation
recursive estimation
Regression
regression analysis
least squares approximations
coupled-least-squares parameter identification algorithm
convergence
multiinput multioutput systems
regression analysis
multivariable recursive least-squares algorithm
convergence theorems
MIMO systems
multivariable RLS algorithm
identification problems
C-LS algorithm
multivariable systems
recursive estimation
parameter estimation
multiple linear regression models
ISSN:1751-8644, 1751-8652
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least-squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C-LS algorithm does not involve the matrix inversion and requires less computationally efforts than the multivariable RLS algorithm, and that the parameter estimates given by the C-LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.
Bibliography:Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122, People's Republic of China
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2012.0171