Paper
14 June 2023 Estimation of parameters for two-stage mixed fuzzy linear regression models
Xiaoli Xu, Pingping Zhang
Author Affiliations +
Proceedings Volume 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023); 1272517 (2023) https://doi.org/10.1117/12.2679155
Event: International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 2023, Suzhou, China
Abstract
Combining nonlinear programming and least squares method, this paper proposes a two-stage mixed fuzzy linear regression model based on distance criterion. In order to ensure that the error of fuzzy estimated value and fuzzy observation value can be reduced when the explanatory variable is a clear number, the fuzzy regression model has a fuzzy adjustment term in addition to the clear regression coefficient. Firstly, based on the distance criterion, a nonlinear programming model is established to obtain the regression coefficient of the explanatory variables, and the fuzzy adjustment term is obtained based on the distance criterion and the least square method. Compared with the existing methods that cannot determine the sign of the coefficient, the method can accurately determine the sign of the regression coefficient. Finally, it is verified that the model has smaller mean square error and higher reliability than other models through a large number of numerical experiments and practical examples.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoli Xu and Pingping Zhang "Estimation of parameters for two-stage mixed fuzzy linear regression models", Proc. SPIE 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 1272517 (14 June 2023); https://doi.org/10.1117/12.2679155
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Fuzzy logic

Performance modeling

Linear regression

Data modeling

Mathematical modeling

Systems modeling

Back to Top