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Soft Computing
Erschienen in: Soft Computing 4/2021

18.09.2020 | Methodologies and Application

Estimating the parameters of fuzzy linear regression model with crisp inputs and Gaussian fuzzy outputs: A goal programming approach

verfasst von: E. Hosseinzadeh, H. Hassanpour

Erschienen in: Soft Computing | Ausgabe 4/2021

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Abstract

In this paper, we offered a new method to fit a fuzzy linear regression model to a set of crisp inputs and Gaussian fuzzy outputs, by considering its parameters as Gaussian fuzzy numbers. To calculate the regression coefficients, a nonlinear programming model is formulated based on a new distance between Gaussian fuzzy numbers. The nonlinear programming model is converted to a goal programming model by choosing appropriate deviation variables and then to a linear programming which can be solved simply by simplex method. To show the efficiency of proposed model, some applicative examples are solved and three simulation studies are performed. The computational results are compared with some earlier methods.
Vorheriger Artikel Computational procedure for solving fuzzy equations
Nächster Artikel -cut set based ranking approach to solving bi-matrix games in neutrosophic environment

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Metadaten
Titel
Estimating the parameters of fuzzy linear regression model with crisp inputs and Gaussian fuzzy outputs: A goal programming approach
verfasst von
E. Hosseinzadeh
H. Hassanpour
Publikationsdatum
18.09.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 4/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-05331-7

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