Development of Product Design Models Using Fuzzy Regression Based Genetic Programming

  • Kit Yan Chan
  • C. K. Kwong
  • Tharam S. Dillon
Part of the Studies in Computational Intelligence book series (SCI, volume 403)


To develop a functional model which relates customer requirements to the design attributes of a new product, a set of customer survey data is usually used. As mentioned in Chapter 5, customer survey data is usually fuzzy in nature, as human feeling is usually fuzzy, and also nonlinearities are unavoidable in the relationships between customer requirements and design attributes. However, the development of explicit functional models has not been addressed by previous studies in modelling the relationships between customer requirements and design attributes. Also, those previous modelling methods can address either nonlinearity or fuzziness only. To overcome the deficiencies of the above approaches, this chapter presents a fuzzy regression based genetic programming method, namely FR-GP, to generate functional models which represent this nonlinear and fuzzy relationships between customer requirements and design attributes.


Mobile Phone Functional Model Customer Requirement Multivariate Adaptive Regression Spline Design Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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