Fuzzy regression models are developed to construct the relationship between explanatory variables and responses in a fuzzy environment. In order to increase the explanatory performance of the model, the least-squares method is applied to determine the numeric coefficients based on the concept of distance. Unlike most existing approaches, the numeric coefficients in the proposed model can have negative values. The proposed model minimizes total estimation error in terms of the sum of the average squared distance between the observed and estimated responses based on a few α-cuts. The proposed approach is not limited to triangular fuzzy numbers; it can be used to carry out a large number of fuzzy observations efficiently because the model is based on traditional statistical methods. Comparisons with existing methods show that based on the total estimation error using the mean squared error and Kim and Bishus criterion, the explanatory performance of the proposed model is satisfactory.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics