NDLI: Nonlinear PCA combining principal curves and RBF-networks for process monitoring

Content Provider	IEEE Xplore Digital Library
Author	Harkat, M.F. Mourot, G. Ragot, J.
Copyright Year	2003
Description	Author affiliation: Centre de Recherche en Autom. de Nancy, UMR-CNRS, Vandoeuvre-les-Nancy, France (Harkat, M.F.; Mourot, G.; Ragot, J.)
Abstract	The use of principal component analysis (PCA) for process monitoring applications has attracted much attention recently. PCA is the optimal linear transformation with respect to minimizing the mean square prediction error but it only considers second order statistics. If the data have nonlinear dependencies, an important issue is to develop a technique which takes higher order statistics into account and which can eliminate dependencies not removed by PCA. Recognizing the shortcomings of PCA, a nonlinear extension of PCA is developed. The purpose of this paper is to present a nonlinear generalization of PCA (NLPCA) by combining the principal curves and RBF-Networks. The NLPCA model consists of two RBF networks where the nonlinear transformations of the input variables (that characterize the nonlinear principal component analysis) are modelled as a linear sum of radially symmetric kernel functions by using the first network. The nonlinear principal component, which represents the desired output of the first network, are obtained by the principal curves algorithm. The second network tries to perform the inverse transformation by reproducing the original data. The proposed approach is illustrated by a simulation example.
Sponsorship	Honeywell Lab. Xerox Nat. Instruments Math Works
Starting Page	1956
Ending Page	1961
File Size	386991
Page Count	6
File Format	PDF
ISBN	0780379241
ISSN	01912216
DOI	10.1109/CDC.2003.1272902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Fault detection Neural networks Higher order statistics Fault diagnosis Redundancy PROM Computerized monitoring Error analysis Radial basis function networks
Content Type	Text
Resource Type	Article

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