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