Fuzzy linear regression models with absolute errors and optimum uncertainty

Various kinds of the fuzzy regression models are introduced in the literature and many different algorithms are proposed to estimate fuzzy parameters of the models. In this study a new approach is introduced to find the parameters of a linear fuzzy regression, the input data of which is measured by crisp numbers. A new objective function is designed and solved, by which a minimum degree of acceptable uncertainty (the h-level or h-cut) is found. Two numerical examples are presented to compare the proposed approach with other methods.