Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/75212
Title: System Identification using Augmented Principal Component Analysis
Authors: Vijaysai, P.
Gudi, R.D.
Lakshminarayanan, S. 
Keywords: Augmented PCA
Collinearity problems
Error-in-variables
Issue Date: 2003
Source: Vijaysai, P.,Gudi, R.D.,Lakshminarayanan, S. (2003). System Identification using Augmented Principal Component Analysis. Proceedings of the American Control Conference 5 : 4179-4184. ScholarBank@NUS Repository.
Abstract: 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 from the literature.
Source Title: Proceedings of the American Control Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/75212
ISSN: 07431619
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

41
checked on Feb 16, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.