Possibilistic linear regression analysis for fuzzy data

Hideo Tanaka, Isao Hayashi, Junzo Watada

Research output: Contribution to journalArticle

268 Citations (Scopus)

Abstract

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming problems, the merit of our formulations is to be able to obtain easily fuzzy parameters in possibilistic linear models and to add other constraint conditions which might be obtained from expert knowledge of fuzzy parameters. This approach can be regarded as a fuzzy interval analysis in a fuzzy environment.

Original languageEnglish
Pages (from-to)389-396
Number of pages8
JournalEuropean Journal of Operational Research
Volume40
Issue number3
DOIs
Publication statusPublished - 1989 Jun 15
Externally publishedYes

Keywords

  • fuzzy data
  • Fuzzy sets
  • possibilistic linear systems
  • regression

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Modelling and Simulation
  • Transportation

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