Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Chan2010506,
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author = "K. Y. Chan and C. K. Kwong and T. C. Fogarty",
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title = "Modeling manufacturing processes using a genetic
programming-based fuzzy regression with detection of
outliers",
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journal = "Information Sciences",
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volume = "180",
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number = "4",
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pages = "506--518",
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year = "2010",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2009.10.007",
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URL = "http://www.sciencedirect.com/science/article/B6V0C-4XFPR3M-3/2/1f27ff77e40dc7d917de59d3555abf36",
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keywords = "genetic algorithms, genetic programming, Fuzzy
regression, Outlier detection, Epoxy dispensing
process",
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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