Manufacturing modeling using an evolutionary fuzzy regression

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@InProceedings{Chan:2011:ieeeFUZZ,
  author =       "K. Y. Chan and T. S. Dillon and S. H. Ling and 
                 C. K. Kwong",
  title =        "Manufacturing modeling using an evolutionary fuzzy
                 regression",
  booktitle =    "IEEE International Conference on Fuzzy Systems (FUZZ
                 2011)",
  year =         "2011",
  month =        "27-30 " # jun,
  address =      "Taipei, Taiwan",
  pages =        "2261--2267",
  size =         "7 pages",
  abstract =     "Fuzzy regression is a commonly used approach for
                 modelling manufacturing processes in which the
                 availability of experimental data is limited. Fuzzy
                 regression can address fuzzy nature of experimental
                 data in which fuzziness is not avoidable while carrying
                 experiments. However, fuzzy regression can only address
                 linearity in manufacturing process systems, but
                 nonlinearity, which is unavoidable in the process,
                 cannot be addressed. In this paper, an evolutionary
                 fuzzy regression which integrates the mechanism of a
                 fuzzy regression and genetic programming is proposed to
                 generate manufacturing process models. It intends to
                 overcome the deficiency of the fuzzy regression, which
                 cannot address nonlinearities in manufacturing
                 processes. The evolutionary fuzzy regression uses
                 genetic programming to generate the structural form of
                 the manufacturing process model based on tree
                 representation which can address both linearity and
                 nonlinearities in manufacturing processes. Then it uses
                 a fuzzy regression to determine outliers in
                 experimental data sets. By using experimental data
                 excluding the outliers, the fuzzy regression can
                 determine fuzzy coefficients which indicate the
                 contribution and fuzziness of each term in the
                 structural form of the manufacturing process model. To
                 evaluate the effectiveness of the evolutionary fuzzy
                 regression, a case study regarding modelling of epoxy
                 dispensing process is carried out.",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 fuzzy regression, fuzzy coefficients, manufacturing
                 modelling, manufacturing process model, fuzzy set
                 theory, manufacturing processes, regression analysis",
  DOI =          "doi:10.1109/FUZZY.2011.6007322",
  ISSN =         "1098-7584",
  notes =        "Also known as \cite{6007322}",
}

Genetic Programming entries for Kit Yan Chan Tharam S Dillon Sing Ho Ling Che Kit Kwong

Citations