Elsevier

Fuzzy Sets and Systems

Volume 126, Issue 3, 16 March 2002, Pages 389-399
Fuzzy Sets and Systems

Fuzzy least-squares linear regression analysis for fuzzy input–output data

https://doi.org/10.1016/S0165-0114(01)00066-5Get rights and content

Abstract

A fuzzy regression model is used in evaluating the functional relationship between the dependent and independent variables in a fuzzy environment. Most fuzzy regression models are considered to be fuzzy outputs and parameters but non-fuzzy (crisp) inputs. In general, there are two approaches in the analysis of fuzzy regression models: linear-programming-based methods and fuzzy least-squares methods. In 1992, Sakawa and Yano considered fuzzy linear regression models with fuzzy outputs, fuzzy parameters and also fuzzy inputs. They formulated multiobjective programming methods for the model estimation along with a linear-programming-based approach. In this paper, two estimation methods along with a fuzzy least-squares approach are proposed. These proposed methods can be effectively used for the parameter estimation. Comparisons are also made between them.

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