Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling

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@InProceedings{marenbach:1997:Evicnf,
  author =       "P. Marenbach and M. Brown",
  title =        "Evolutionary versus inductive construction of
                 neurofuzzy systems for bioprocess modelling",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  editor =       "Ali Zalzala",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "Institution of Electrical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-85296-693-8",
  URL =          "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_97_11.pdf",
  URL =          "http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=681045",
  abstract =     "The control and optimization of biotechnological
                 processes is a complex task of industrial relevance,
                 due to the growing importance attached to
                 biotechnology. Therefore, there is an increasing use of
                 intelligent data analysis methods for the development
                 and optimization of bioprocess modelling and control.
                 Since a clear understanding of the underlying physics
                 does not exist, nonlinear learning systems, which can
                 accurately model exemplar data sets and explain their
                 behaviour to the designer, are an attractive approach.
                 This paper investigates applying neurofuzzy
                 construction algorithms to this problem and in
                 particular compares a Genetic Programming structuring
                 approach with a more conventional forwards inductive
                 learning-type algorithm. It is shown that for simple
                 problems, the inductive learning technique generally
                 outperforms the Genetic Programming, although for large
                 complex problems, the latter may prove beneficial.",
  notes =        "GALESIA'97",
}

Genetic Programming entries for Peter Marenbach M Brown

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