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.
- algorithms,
- analysis,
- chemical
- columns,
- component
- data
- distillation
- genetic
- mathematical
- methods,
- multivariate
- nonlinear
- operations,
- plants,
- principal
- programming,
- reduction,
- statistical
- statistics
- statistics,
- systems,
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