A fuzzy rule based personal Kansei modeling system

Hajime Hotta, Masafumi Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

A personal Kansei modeling (PKM) system is proposed in this paper. In Kansei modeling, tendency that is common to group members is usually discussed. However, treating personal tendency is becoming more and more important. With this system, a set of fuzzy rules are extracted through the analysis of Kansei data such as questionnaire responses. Generally, the amount of Kansei data taken from one person tends to be too small to analyze his/her Kansei. Basic idea of PKM system proposed in this paper is to create a common Kansei model from group data (first stage) before creating a personal Kansei model from personal data (second stage). In order to create a common Kansei model in the first stage, variance predictable general regression neural network (VPGRNN), which is an enhanced version of GRNN, and Fuzzy Adaptive Resonance Theory (Fuzzy ART) are employed in this system. A common model consists of a set of fuzzy rules, each associated with an adjustment factor, for the second stage. In the second stage, the fuzzy rules in the common model are adjusted using personal Kansei data to produce a set of fuzzy rules composing a personal Kansei model.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Fuzzy Systems
Pages1031-1037
Number of pages7
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 IEEE International Conference on Fuzzy Systems - Vancouver, BC, Canada
Duration: 2006 Jul 162006 Jul 21

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2006 IEEE International Conference on Fuzzy Systems
CountryCanada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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