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System identification using augmented principal component analysis

DSpace at IIT Bombay

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Title System identification using augmented principal component analysis
 
Creator VIJAYSAI, P
GUDI, RD
LAKSHMINARAYANAN, S
 
Subject problem solving
mathematical operators
principal component analysis
identification (control systems)
 
Description The total least squares (TLS) technique has been extensively used for the identification of dynamic systems when both the inputs and outputs are corrupted with noise. But the major limitation of this technique has been the difficulty in identifying the actual parameters when the collinearity in the input data leads to several "small" eigenvalues. This paper proposes a novel technique namely augmented principal component analysis (APCA) to deal with collinearity problems in the error-in-variable formulation. The APCA formulation can also be used to determine the least squares prediction error when an appropriate operator is chosen. This property has been used for the nonlinear structure selection through forward selection methodology. The efficacy of the new technique has been illustrated through representative case studies taken form the literature.
 
Publisher IEEE
 
Date 2009-03-05T08:21:52Z
2011-11-28T07:38:34Z
2011-12-15T09:57:08Z
2009-03-05T08:21:52Z
2011-11-28T07:38:34Z
2011-12-15T09:57:08Z
2003
 
Type Article
 
Identifier Proceedings of the American Control Conference (V 5), Denver, USA, 4-6 June 2003, 4179-4184
0-7803-7896-2
10.1109/ACC.2003.1240491
http://hdl.handle.net/10054/862
http://dspace.library.iitb.ac.in/xmlui/handle/10054/862
 
Language en