Using an evolutionary fuzzy regression for affective product design

K. Y. Chan, T. S. Dillon, Chun Kit Kwong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In affective product design, one of the main goals is to maximize customers' affective satisfaction by optimizing design variables of a new product. To achieve this, a model in relating customers' affective responses and design variables of a new product is required to be developed based on customers' survey data. However, previous research on modelling the relationship between affective response and design variables cannot address the development of explicit models either involving nonlinearity or fuzziness, which exist in customers' survey data. In this paper, an evolutionary fuzzy regression approach is proposed to generate explicit models to represent this nonlinear and fuzzy relationship between affective responses and design variables. In the approach, genetic programming is used to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. Fuzzy coefficients of the model, which is represented by the tree, are determined based on a fuzzy regression algorithm. As a result, the fuzzy nonlinear regression model can be obtained to relate affective responses and design variables
Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010
DOIs
Publication statusPublished - 25 Nov 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Country/TerritorySpain
CityBarcelona
Period18/07/1023/07/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Cite this