Evaluation of fuzzy linear regression models by comparing membership functions

Byungjoon Kim, Ram R. Bishu

Research output: Contribution to journalArticlepeer-review

157 Scopus citations


Fuzzy linear regression models can provide an estimated fuzzy number that has a fuzzy membership function. If a point that has the highest membership value from the estimated fuzzy number is not within the support of the observed fuzzy membership function, a decision-maker can have high risk from the estimate. In this study a modification of fuzzy linear regression analysis based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers is proposed. Two numerical examples are used to evaluate the fuzzy regression models.

Original languageEnglish (US)
Pages (from-to)343-352
Number of pages10
JournalFuzzy Sets and Systems
Issue number1-3
StatePublished - 1998


  • Fuzzy linear regression
  • Fuzzy numbers
  • Support

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence


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