Fuzzy linear regression for the data which is fuzzified with SMGRT method

28294

In classical regression analysis a difference is occurred between the observed dependent variable and estimates of the generated models. This difference is generally caused by the assumption of that the observed dependent variable has to be normally distributed with a constant variance and zero (0) average. In fuzzy regression analysis this difference is considered to be the blurring of the model structure. for the data which is appropriate for the fuzzy regression analysis instead of classical regression analysis due to certain restrictions, if the data does not include fuzzy variables, using probabilistic models may become mandatory. However, because of certain restrictions there are cases using of probabilistic model is not appropriate. We generate a fuzzy linear regression equation with Fuzzy Least Square Regression (FLSR) model. Here nonfuzzy variables are fuzzified with a new method SMRGT to use models have fuzzy variables. In conclusion, classical linear regression and fuzzy linear regression were applied a data set, and these two approaches performances were compared by using different measures.

152

Fuzzy Regression , Probabilistic Model , FLSR Model , Diamond Model , SMRGT Method

Erciyes University Journal Of The Institute Of Science an‎d Technology

158

Erciyes University Journal Of The Institute Of Science an‎d Technology