Least square regression in intuitionistic fuzzy environment with crisp coefficients with the ability to determine the decision level

In practical problems where imprecise components are an inseparable part of them, using fuzzy sets to check their features and measure their efficiency solves some of the current limitations. In this article, we examine the problem of linear regression in an intuitionistic fuzzy environment. Here, we consider the input and output data as intuitionistic fuzzy numbers and assume that the coefficients of the model are crisp numbers. Since to correctly determine the coefficients, we need to calculate the distance between the output of the model and the output data, we present a new parametric distance to calculate the distance between intuitionistic fuzzy numbers. The salient point in this article is that the researcher can estimate the parameter values of the model based on different decision-making levels. To show the efficiency and test the proposed method, several examples are presented at the end of the article. The results are calculated and compared based on the values of different levels of decision-making.

کلیدواژه ها:

Distance ، Fuzzy regression ، Intuitionistic fuzzy