Non-linear principal components analysis using genetic programming

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@Article{hiden:1999:CCE,

• author = "H. G. Hiden and M. J. Willis and M. T. Tham and G. A. Montague",
• title = "Non-linear principal components analysis using genetic programming",
• 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

Citations