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Non-linear principal components analysis using genetic programming

Lookup NU author(s): Dr Hugo Hiden, Dr Mark Willis, Dr Ming Tham, Professor Gary Montague

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Abstract

Principal components analysis (PCA) is a standard statistical technique, which is frequently employed in the analysis of large highly correlated data sets. As it stands, PCA is a linear technique which can limit its relevance to the non-linear systems frequently encountered in the chemical process industries. Several attempts to extend linear PCA to cover non-linear data sets have been made, and will be briefly reviewed in this paper. We propose a symbolically oriented technique for non-linear PCA, which is based on the genetic programming (GP) paradigm. Its applicability will be demonstrated using two simple non-linear systems and data collected from an industrial distillation column.


Publication metadata

Author(s): Hiden HG; Willis MJ; Montague GA; Tham MT

Publication type: Article

Publication status: Published

Journal: Computers and Chemical Engineering

Year: 1999

Volume: 23

Issue: 3

Pages: 413-425

Print publication date: 28/02/1999

ISSN (print): 0098-1354

ISSN (electronic): 1873-4375

Publisher: Elsevier Science Ltd

URL: http://dx.doi.org/10.1016/S0098-1354(98)00284-1

DOI: 10.1016/S0098-1354(98)00284-1


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