A Fuzzy Goal Programming Approach to Fully Fuzzy Linear Regression

被引：2

作者：

Perez-Canedo, Boris ^{[1
]}

Rosete, Alejandro ^{[2
]}

Verdegay, Jose Luis ^{[3
]}

Concepcion-Morales, Eduardo Rene ^{[1
]}

机构：

[1] Univ Cienfuegos, Cienfuegos 55100, Cuba

[2] Technol Univ Havana, Havana, Cuba

[3] Univ Granada, Granada, Spain

来源：

INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2020, PT II | 2020年 / 1238卷

关键词：

Fully fuzzy linear regression; Fully fuzzy multiobjective linear programming; Fuzzy goal programming; Linear scalarisation; Chebyshev scalarisation; REASONABLE PROPERTIES; FIND;

D O I：

10.1007/978-3-030-50143-3_53

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traditional linear regression analysis aims at finding a linear functional relationship between predictor and response variables based on available data of a given system, and, when this relationship is found, it is used to predict the future behaviour of the system. The difference between the observed and predicted data is supposed to be due to measurement errors. In fuzzy linear regression, on the other hand, this difference is supposed to be mainly due to the indefiniteness of the system. In this paper, we assume that predictor and response variables are LR-type fuzzy numbers, and so are all regression coefficients; this is known as fully fuzzy linear regression (FFLR) problem. We transform the FFLR problem into a fully fuzzy multiobjective linear programming (FFMOLP) problem. Two fuzzy goal programming methods based on linear and Chebyshev scalarisations are proposed to solve the FFMOLP problem. The proposed methods are compared with a recently published method and show promising results.

引用

页码：677 / 688

页数：12

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