Intuitionistic fuzzy linear regression analysis

• Published in: Fuzzy optimization and decision making Vol. 12; no. 2; pp. 215 - 229
• Main Authors: Parvathi, R., Malathi, C., Akram, M., Atanassov, Krassimir T.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.06.2013; Springer Nature B.V
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Decision analysis; Fuzzy; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Intermediate frequency; Linear programming; Mathematical Logic and Foundations; Mathematical models; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Probability Theory and Stochastic Processes; Regression; Regression analysis; Science; Set theory; Studies; Fuzzy sets; Linear programming; Symmetric triangular intuitionistic fuzzy numbers; Decision analysis; Uncertainty modelling; Intuitionistic fuzzy regression analysis
• ISSN: 1568-4539; 1573-2908
• DOI: 10.1007/s10700-012-9150-9

Linear regression analysis in an intuitionistic fuzzy environment using intuitionistic fuzzy linear models with symmetric triangular intuitionistic fuzzy number (STriIFN) coefficients is introduced. The goal of this regression is to find the coefficients of a proposed model for all given input–output data sets. The coefficients of an intuitionistic fuzzy regression (IFR) model are found by solving a linear programming problem (LPP). The objective function of the LPP is to minimize the total fuzziness of the IFR model which is related to the width of IF coefficients. An illustrative example is also presented to depict the solution procedure of the IFR problem by using STriIFNs.