Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33301
Title: Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers
Authors: Chan, KY
Kwong, CK 
Fogarty, TC
Keywords: Epoxy dispensing process
Fuzzy regression
Genetic programming
Outlier detection
Issue Date: 2010
Publisher: Elsevier
Source: Information sciences, 2010, v. 180, no. 4, p. 506-518 How to cite?
Journal: Information sciences 
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.
URI: http://hdl.handle.net/10397/33301
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2009.10.007
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

31
Last Week
0
Last month
0
Citations as of Aug 18, 2017

WEB OF SCIENCETM
Citations

25
Last Week
0
Last month
0
Citations as of Aug 20, 2017

Page view(s)

40
Last Week
3
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.