Steady-state Modelling of Chemical Process System using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{mckay:1996:ssmcps,
  author =       "Ben McKay and Mark Willis and Geoffrey Barton",
  title =        "Steady-state Modelling of Chemical Process System
                 using Genetic Programming",
  journal =      "Computers and Chemical Engineering",
  year =         "1997",
  volume =       "21",
  number =       "9",
  pages =        "981--996",
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, process modelling",
  URL =          "http://www.sciencedirect.com/science/article/B6TFT-3S9TDFC-5/2/339ca8a827eb95c025f2fe7bf8054f1c",
  ISSN =         "0098-1354",
  DOI =          "doi:10.1016/S0098-1354(96)00329-8",
  size =         "16 pages",
  abstract =     "Complex processes are often modelled using
                 input-output data from experimental tests. Regression
                 and neural network modelling techniques are commonly
                 used for this purpose. Unfortunately, these methods
                 provide minimal information about the model structure
                 required to accurately represent process
                 characteristics. In this contribution, we propose the
                 use of Genetic Programming (GP) as a method for
                 developing input-output process models from
                 experimental data. GP performs symbolic regression,
                 determining both the structure and the complexity of
                 the model during its evolution. This has the advantage
                 that no a priori modelling assumptions have to be made.
                 Moreover, the technique can discriminate between
                 relevant and irrelevant process inputs, yielding
                 parsimonious model structures that accurately represent
                 process characteristics. Following a tutorial example,
                 the usefulness of the technique is demonstrated by the
                 development of steady-state models for two typical
                 processes, a vacuum distillation column and a chemical
                 reactor system. A statistical analysis procedure is
                 used to aid in the assessment of GP algorithm settings
                 and to guide in the selection of the final model
                 structure.",
}

Genetic Programming entries for Ben McKay Mark J Willis Geoffrey W Barton

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