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Fuzzy linear regression models with least square errors

delete2005-04-01
delete70
PRE
AI
M
Mohammad Modarres *
E
Ebrahim Nasrabadi
M
Mohammad Mehdi Nasrabadi
DOI：10.1016/j.amc.2004.05.004delete
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Abstract

Abstract

En 中文
To estimate the parameters of fuzzy linear regression models with fuzzy output and crisp inputs, we develop a mathematical programming model in this paper. The method is constructed on the basis of minimizing the square of the total difference between observed and estimated spread values or in other words minimizing the least square errors. The advantage of the proposed approach is its simplicity in programming and computation as well as its performance. To compare the performance of the proposed approach with the other methods, two examples are presented. (c) 2004 Elsevier Inc. All rights reserved.
Keywords:
fuzzy numbers
fuzzy linear regression
mathematical programming

Journal

Applied Mathematics and Computation cover
Applied Mathematics and Computation
IF:
3.4
Papers:
2.3W
Citations:
3.3W

Organization

No organization information available