Development of customer satisfaction models for affective design using rough set and ANFIS approaches

Huimin Jiang, Chun Kit Kwong, M. C. Law, W. H. Ip

Research output: Journal article publicationConference articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Rough set (RS)- and particle swarm optimization (PSO)- based adaptive neuro-fuzzy inference system (ANFIS) approaches are proposed to generate customer satisfaction models in affective design that address fuzzy and nonlinear relationships between affective responses and design attributes. The RS theory is adopted to reduce the number of fuzzy rules generated using ANFIS and simplify the structure of ANFIS. PSO is employed to determine the parameter settings of an ANFIS from which customer satisfaction models with better modeling accuracy can be generated. A case study of mobile phone affective design is used to illustrate the proposed approaches.
Original languageEnglish
Pages (from-to)104-112
Number of pages9
JournalProcedia Computer Science
Volume22
DOIs
Publication statusPublished - 1 Jan 2013
Event17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013 - Kitakyushu, Japan
Duration: 9 Sep 201311 Sep 2013

Keywords

  • Affective design
  • ANFIS
  • Customer satisfaction
  • Particle swarm optimization
  • Rough set theory

ASJC Scopus subject areas

  • Computer Science(all)

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