This article establishes an intuitionistic fuzzy linear regression model (IFLRM) under the consideration that the explanatory and response variables in the observation data set as well as the parameters of the model are intuitionistic fuzzy numbers (IFNs). The weakest T-norm arithmetic is applied in the formulation of the IFLRMs to avoid wide spreads in the predicted IFN responses. The sign of the parameters is determined in the formulation process. We propose a mathematical programming problem to find the optimal IFN parameters. The goal of the optimization is to minimize the absolute distances between the observed and predicted IFNs. To enhance computational efficiency, a three-step procedure is proposed for solving a mathematical programming problem when the number of explanatory variables or the size of the observation data set is large. Comparisons with existing approaches indicate that the proposed approach has outstanding performance in terms of similarity and distance measures.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics