A mathematical-programming approach to fuzzy linear regression analysis

2004 ◽  
Vol 155 (3) ◽  
pp. 873-881 ◽  
Author(s):  
Mohammad Mehdi Nasrabadi ◽  
Ebrahim Nasrabadi
2012 ◽  
Vol 12 (2) ◽  
pp. 215-229 ◽  
Author(s):  
R. Parvathi ◽  
C. Malathi ◽  
M. Akram ◽  
Krassimir T. Atanassov

Author(s):  
Kazuhisa Takemura ◽  

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.


2016 ◽  
Vol 44 ◽  
pp. 156-167 ◽  
Author(s):  
Lingjian Yang ◽  
Songsong Liu ◽  
Sophia Tsoka ◽  
Lazaros G. Papageorgiou

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