Evaluation of fuzzy linear regression models by comparing membership functions

Byungjoon Kim, Ram R. Bishu

Research output: Contribution to journalArticle

129 Citations (Scopus)

Abstract

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
Volume100
Issue number1-3
DOIs
StatePublished - Jan 1 1998

Fingerprint

Fuzzy Linear Regression
Membership functions
Fuzzy numbers
Linear Regression Model
Membership Function
Linear regression
Fuzzy Membership Function
Evaluation
Regression analysis
Fuzzy Regression
Fuzzy Membership
Fuzzy Model
Regression Analysis
Regression Model
Numerical Examples
Evaluate
Estimate
Linear regression model
Membership function

Keywords

  • Fuzzy linear regression
  • Fuzzy numbers
  • Support

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence

Cite this

Evaluation of fuzzy linear regression models by comparing membership functions. / Kim, Byungjoon; Bishu, Ram R.

In: Fuzzy Sets and Systems, Vol. 100, No. 1-3, 01.01.1998, p. 343-352.

Research output: Contribution to journalArticle

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