Abstract
Previous works on fuzzy regression models have been developed as an extension of the interval regression model. On account of this line of approach, in previous works, observed data of dependent variables are not contained in minimizing objectives but in constraint conditions only. As a result, the estimated fuzzy numbers that are derived from those models can be said that they have little reflection of the dependent fuzzy numbers used for identifying models. In this paper, the author proposes a method for identifying fuzzy regression models. My method is based on the concept of least square estimate as well as interval linear regression of the previous type, and include two types of problems to solve. The first type problem is given as a single-stage problem, and the second is given as a two-stage one. Since observed data are explicitly taken into objective functions in both problems, fuzzy regression models identified by our method are more sensitive, with respect to observed data, than those of previous method. And the author can compare models by defining the function that judges models. With this function, the author can quantify the difference between previous model and new one.
