Non-linear principal components analysis using genetic programming

Principal components analysis (PCA) is a standard statistical technique, wh ich 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 i ndustries. Several attempts to extend linear PCA to cover non-linear data s ets 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 d istillation column. (C) 1999 Elsevier Science Ltd. All rights reserved.