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

K. Y. Chan, Chun Kit Kwong, T. C. Fogarty

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)506-518
Number of pages13
JournalInformation Sciences
Issue number4
Publication statusPublished - 15 Feb 2010


  • Epoxy dispensing process
  • Fuzzy regression
  • Genetic programming
  • Outlier detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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