A genetic programming based fuzzy regression approach to modelling manufacturing processes

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@Article{Chan:2010:IJPR,
  author =       "K. Y. Chan and C. K. Kwong and Y. C. Tsim",
  title =        "A genetic programming based fuzzy regression approach
                 to modelling manufacturing processes",
  journal =      "International Journal of Production Research",
  year =         "2010",
  volume =       "48",
  number =       "7",
  pages =        "1967--1982",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming fuzzy
                 regression, process modelling, solder paste
                 dispensing",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/00207540802644845",
  URL =          "http://www.tandfonline.com/doi/pdf/10.1080/00207540802644845",
  DOI =          "doi:10.1080/00207540802644845",
  size =         "16 pages",
  abstract =     "Fuzzy regression has demonstrated its ability to model
                 manufacturing processes in which the processes have
                 fuzziness and the number of experimental data sets for
                 modelling them is limited. However, previous studies
                 only yield fuzzy linear regression based process models
                 in which variables or higher order terms are not
                 addressed. In fact, it is widely recognised that
                 behaviours of manufacturing processes do often carry
                 interactions among variables or higher order terms. In
                 this paper, a genetic programming based fuzzy
                 regression GP-FR, is proposed for modelling
                 manufacturing processes. The proposed method uses the
                 general outcome of GP to construct models the structure
                 of which is based on a tree representation, which could
                 carry interaction and higher order terms. Then, a fuzzy
                 linear regression algorithm is used to estimate the
                 contributions and the fuzziness of each branch of the
                 tree, so as to determine the fuzzy parameters of the
                 genetic programming based fuzzy regression model. To
                 evaluate the effectiveness of the proposed method for
                 process modelling, it was applied to the modelling of a
                 solder paste dispensing process. Results were compared
                 with those based on statistical regression and fuzzy
                 linear regression. It was found that the proposed
                 method can achieve better goodness-of-fitness than the
                 other two methods. Also the prediction accuracy of the
                 model developed based on GP-FR is better than those
                 based on the other two methods.",
}

Genetic Programming entries for Kit Yan Chan Che Kit Kwong Y C Tsim

Citations