Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/81032


Title: On the Optimization Methods for Fully Fuzzy Regression Models
Authors: Wu, Berlin
吳柏林
Liu, Hsuan-Ku
Hsu, Yu-Yun
Contributors: 應數系
Keywords: Fuzzy regression;Fuzzy parameter;H-cut;Methods of least square;Triangular membership function
Date: 2010-03
Issue Date: 2016-02-01 16:07:15 (UTC+8)
Abstract: The goal of this paper is to construct a multiple fuzzy regression model by fuzzy parameters estimation using the fuzzy samples. We propose an optimization model that provides fuzzy coefficients to minimize the distance between the fuzzy regressands and the fuzzy regressors. It concerned with imprecise measurement of observed variables, linear programming estimation and non-parametric methods. This is different from the assumptions as well as the estimation techniques of the classical analysis. Empirical results demonstrate that our new approach is efficient and more realistic than the traditional regression analysis did.
Relation: International Journal of Intelligent Technologies & Applied Statistics, 3(1), 45-55
Data Type: article
Appears in Collections:[應用數學系] 期刊論文

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