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Errors-in-variables-based modeling using augmented principal components

DSpace at IIT Bombay

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Title Errors-in-variables-based modeling using augmented principal components
 
Creator VIJAYSAI, P
GUDI, RD
LAKSHMINARAYANAN, S
 
Subject least-squares regression
distillation compositions
adaptive-control
identification
pls
algorithms
systems
 
Description The total least-squares technique has been extensively used for the identification of dynamic systems when both the inputs and outputs are corrupted with noise. However, the major limitation of this technique has been the difficulty in identifying the actual and stable model parameters when the collinearity in the causal data block leads to several "small" singular values. This paper proposes a novel multivariate tool, namely, the augmented principal components analysis (APCA), to deal with collinearity problems under the errors-in-variables formulation. On the basis of the level of noise in each measured variable, the proposed technique is divided further into simple and generalized augmented principal components. Some of the errors-invariables- and least-squares-based methods available in the literature have been shown as special cases of this APCA-based technique. The efficacy of the new technique over other conventional methods has been illustrated through representative case studies taken from the literature.
 
Publisher AMER CHEMICAL SOC
 
Date 2011-07-14T02:08:23Z
2011-12-26T12:47:30Z
2011-12-27T05:35:25Z
2011-07-14T02:08:23Z
2011-12-26T12:47:30Z
2011-12-27T05:35:25Z
2005
 
Type Article
 
Identifier INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 44(2), 368-380
0888-5885
http://dx.doi.org/10.1021/ie030671g
http://dspace.library.iitb.ac.in/xmlui/handle/10054/3831
http://hdl.handle.net/10054/3831
 
Language en