Fuzzy Regression Analysis with a proposed model

Regression analysis refers to methods by which estimates are made for the model parameters from the knowledge of the values of a given input-output data set. The aim of this research this research is to find a suitable model and determine the ‘best’ values of the parameters of the model from the given data. In the statistical regression analysis, deviations between the observed output values and corresponding values predicted by the model are attributed to random errors. It is often assumed that the distribution of these random errors is Gaussian. On the other hand, in fuzzy regression analysis the deviations (errors) are attributed to the imprecision or the vagueness of the system structure or data. The research proposed a new fuzzy linear programming model. The new proposed model is compared with the models used in the literature which are Tanaka, Hojati and Tansu regression models. The results are presented and comparison has been done for each model. Eleven different applications have been mentioned. Then the comparison of results of all the application regarding each similarity measure of goodness of fit is stated in the paper.