Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

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

@Article{Chan2010506,
  author =       "K. Y. Chan and C. K. Kwong and T. C. Fogarty",
  title =        "Modeling manufacturing processes using a genetic
                 programming-based fuzzy regression with detection of
                 outliers",
  journal =      "Information Sciences",
  volume =       "180",
  number =       "4",
  pages =        "506--518",
  year =         "2010",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2009.10.007",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-4XFPR3M-3/2/1f27ff77e40dc7d917de59d3555abf36",
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 regression, Outlier detection, Epoxy dispensing
                 process",
  abstract =     "Fuzzy regression (FR) been demonstrated as a promising
                 technique for modeling manufacturing processes where
                 availability of data is limited. FR can only yield
                 linear type FR models which have a higher degree of
                 fuzziness, but FR ignores higher order or interaction
                 terms and the influence of outliers, all of which
                 usually exist in the manufacturing process data.
                 Genetic programming (GP), on the other hand, can be
                 used to generate models with higher order and
                 interaction terms but it cannot address the fuzziness
                 of the manufacturing process data. In this paper,
                 genetic programming-based fuzzy regression (GP-FR),
                 which combines the advantages of the two approaches to
                 overcome the deficiencies of the commonly used existing
                 modeling methods, is proposed in order to model
                 manufacturing processes. GP-FR uses GP to generate
                 model structures based on tree representation which can
                 represent interaction and higher order terms of models,
                 and it uses an FR generator based on fuzzy regression
                 to determine outliers in experimental data sets. It
                 determines the contribution and fuzziness of each term
                 in the model by using experimental data excluding the
                 outliers. To evaluate the effectiveness of GP-FR in
                 modeling manufacturing processes, it was used to model
                 a non-linear system and an epoxy dispensing process.
                 The results were compared with those based on two
                 commonly used FR methods, Tanka's FR and Peters' FR.
                 The prediction accuracy of the models developed based
                 on GP-FR was shown to be better than that of models
                 based on the other two FR methods.",
}

Genetic Programming entries for Kit Yan Chan Che Kit Kwong Terence C Fogarty

Citations