A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS

Authors

  • H. Hassanpour Department of Mathematics, University of Birjand, Birjand, Iran
  • H. R. Malek Faculty of Basic Sciences, Shiraz University of Technology, Shiraz, Iran
  • M. A. Yaghoobi Department of Statistics, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract:

Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) proposeda modification of fuzzy linear regression analysis. Their modificationis based on a criterion of minimizing the difference of the fuzzy membershipvalues between the observed and estimated fuzzy numbers. We show that theirmethod often does not find acceptable fuzzy linear regression coefficients andto overcome this shortcoming, propose a modification. Finally, we present twonumerical examples to illustrate efficiency of the modified method.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

a note on evaluation of fuzzy linear regression models by comparing membership functions

kim and bishu (fuzzy sets and systems 100 (1998) 343-352) proposeda modification of fuzzy linear regression analysis. their modificationis based on a criterion of minimizing the difference of the fuzzy membershipvalues between the observed and estimated fuzzy numbers. we show that theirmethod often does not find acceptable fuzzy linear regression coefficients andto overcome this shortcoming, pr...

full text

Evaluation of fuzzy linear regression models by comparing membership functions

Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) proposed a modification of fuzzy linear regression analysis. Their modification is based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers. We show that their method often does not find acceptable fuzzy linear regression coefficients and to overcome this shortcomin...

full text

SOLVING FUZZY LINEAR PROGRAMMING PROBLEMS WITH LINEAR MEMBERSHIP FUNCTIONS-REVISITED

Recently, Gasimov and Yenilmez proposed an approach for solving two kinds of fuzzy linear programming (FLP) problems. Through the approach, each FLP problem is first defuzzified into an equivalent crisp problem which is non-linear and even non-convex. Then, the crisp problem is solved by the use of the modified subgradient method. In this paper we will have another look at the earlier defuzzifi...

full text

Evaluation of Fuzzy Linear Regression Models by Parametric Distance

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 decisionmaker can have high risk from the estimate. In this study a new distance, between fuzzy numbers is proposed. On the basis of this ...

full text

solving fuzzy linear programming problems with linear membership functions-revisited

recently, gasimov and yenilmez proposed an approach for solving two kinds of fuzzy linear programming (flp) problems. through the approach, each flp problem is first defuzzified into an equivalent crisp problem which is non-linear and even non-convex. then, the crisp problem is solved by the use of the modified subgradient method. in this paper we will have another look at the earlier defuzzifi...

full text

On fuzzy goal programming with piecewise linear membership functions

This paper deals with the adaptation to fuzzy goal programming of the most used methods to formulate, as a linear programming problem, a classic goal programming problem with piecewise linear penalty functions. Up to now, the literature has been only dealing with the adaptation of the Charnes and Cooper’s method. In this paper we show that the mentioned adaptations are not completely correct. W...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 6  issue 2

pages  1- 6

publication date 2009-06-10

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023