Non-linear principal components analysis using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "H. G. Hiden and M. J. Willis and M. T. Tham and 
                 G. A. Montague",
  title =        "Non-linear principal components analysis using genetic
  journal =      "Computers and Chemical Engineering",
  year =         "1999",
  volume =       "23",
  number =       "3",
  pages =        "413--425",
  month =        "28 " # feb,
  keywords =     "genetic algorithms, genetic programming, data
                 analysis, multivariate statistics, statistical methods,
                 data reduction, mathematical programming, distillation
                 columns, nonlinear systems, chemical operations,
                 chemical plants, principal component analysis,
                 multivariate statistics",
  DOI =          "doi:10.1016/S0098-1354(98)00284-1",
  size =         "13 pages",
  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.",
  notes =        "Matlab, Maple, pop=60",

Genetic Programming entries for Hugo Hiden Mark J Willis Ming T Tham Gary A Montague