A goal programming approach to fuzzy linear regression with fuzzy input–output data

2011 ◽  
Vol 15 (8) ◽  
pp. 1569-1580 ◽  
Author(s):  
H. Hassanpour ◽  
H. R. Maleki ◽  
M. A. Yaghoobi
Keyword(s):  
Linear Regression ◽  
Goal Programming ◽  
Programming Approach ◽  
Input Output ◽  
Fuzzy Linear Regression ◽  
Output Data ◽  
Fuzzy Input

A GOAL PROGRAMMING APPROACH TO FUZZY LINEAR REGRESSION WITH NON-FUZZY INPUT AND FUZZY OUTPUT DATA

2009 ◽  
Vol 26 (05) ◽  
pp. 587-604 ◽  
Author(s):  
H. HASSANPOUR ◽  
H. R. MALEKI ◽  
M. A. YAGHOOBI
Keyword(s):  
Linear Regression ◽  
Goal Programming ◽  
Programming Approach ◽  
Simulation Studies ◽  
Fuzzy Linear Regression ◽  
Output Data ◽  
The Past ◽  
Proposed Model ◽  
Fuzzy Input ◽  
Fuzzy Linear Regression Model

Many researches have been carried out in fuzzy linear regression since the past three decades. In this paper, a fuzzy linear regression model based on goal programming is proposed. The proposed model takes into account the centers of fuzzy data as an important feature as well as their spreads. Furthermore, the model can deal with both symmetric and non-symmetric data. To show the efficiency of proposed model, it is compared with some earlier methods based on simulation studies and numerical examples. Moreover, the sensitivity of the model to outliers is discussed.

A Fuzzy Linear Regression Analysis for Fuzzy Input-Output Data Using the Least Squares Method under Linear Constraints and Its Application to Fuzzy Rating Data

1999 ◽  
Vol 3 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Kazuhisa Takemura ◽  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Least Squares ◽  
Least Squares Method ◽  
Linear Regression Analysis ◽  
Linear Constraints ◽  
Fuzzy Linear Regression ◽  
Output Data ◽  
Rating Data ◽  
Fuzzy Input

Fuzzy linear regression analysis using the least squares method under linear constraint, where input data, output data, and coefficients are represented by triangular fuzzy numbers, was proposed and compared to possibilistic linear regression analysis proposed by Sakawa and Yano (1992) using fuzzy rating data in a psychological study. Major findings of the comparison were as follows: (1) Under the proposed analysis, the width between the maximum and minimum of the predicted model was nearer to the width of the dependent variable than that of possibilistic linear regression analysis, (2) the representative prediction by the proposed analysis was also nearer to that of the dependent variable, compared to that of possibilistic linear regression analysis.

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