%0 Journal Article %1 hiden:1999:CCE %A Hiden, H. G. %A Willis, M. J. %A Tham, M. T. %A Montague, G. A. %D 1999 %J Computers and Chemical Engineering %K algorithms, analysis, chemical columns, component data distillation genetic mathematical methods, multivariate nonlinear operations, plants, principal programming, reduction, statistical statistics statistics, systems, %N 3 %P 413--425 %R doi:10.1016/S0098-1354(98)00284-1 %T Non-linear principal components analysis using genetic programming %V 23 %X 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.

@article{hiden:1999:CCE, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Hiden, H. G. and Willis, M. J. and Tham, M. T. and Montague, G. A.}, biburl = {https://www.bibsonomy.org/bibtex/2e2b6acfc8ede3b37cd7612f3372718e2/brazovayeye}, doi = {doi:10.1016/S0098-1354(98)00284-1}, interhash = {b189d471107c1bb0f13b5358725013ab}, intrahash = {e2b6acfc8ede3b37cd7612f3372718e2}, journal = {Computers and Chemical Engineering}, keywords = {algorithms, analysis, chemical columns, component data distillation genetic mathematical methods, multivariate nonlinear operations, plants, principal programming, reduction, statistical statistics statistics, systems,}, month = {28 February}, notes = {Matlab, Maple, pop=60}, number = 3, pages = {413--425}, size = {13 pages}, timestamp = {2008-06-19T17:41:25.000+0200}, title = {Non-linear principal components analysis using genetic programming}, volume = 23, year = 1999 }