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...

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Veröffentlicht in:	IET control theory & applications Jg. 7; H. 1; S. 68 - 79
1. Verfasser:	Ding, Feng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Stevenage The Institution of Engineering and Technology 01.01.2013 John Wiley & Sons, Inc
Schlagworte:	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-Zugang:	Volltext
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