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Soft Computing
Erschienen in: Soft Computing 8/2011

01.08.2011 | Original Paper

A goal programming approach to fuzzy linear regression with fuzzy input–output data

verfasst von: H. Hassanpour, H. R. Maleki, M. A. Yaghoobi

Erschienen in: Soft Computing | Ausgabe 8/2011

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Abstract

Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered in outputs and/or in inputs. This paper focuses on both fuzzy inputs and fuzzy outputs. First, some approximations for multiplication of two triangular fuzzy numbers are introduced. Then, to evaluate the fuzzy linear regression, the best approximation is selected to minimize a suitable function via goal programming. An important feature of the proposed model is that it takes into account the centers of fuzzy data as well as their spreads. Moreover, it is flexible to deal with both symmetric and non-symmetric data. Furthermore, it can handle the crisp inputs and trapezoidal fuzzy outputs easily. To show the efficiency of the proposed model, some numerical examples are solved and compared with some earlier methods.
Vorheriger Artikel Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control
Nächster Artikel Classification-based self-adaptive differential evolution with fast and reliable convergence performance

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Metadaten
Titel
A goal programming approach to fuzzy linear regression with fuzzy input–output data
verfasst von
H. Hassanpour
H. R. Maleki
M. A. Yaghoobi
Publikationsdatum
01.08.2011
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 8/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-010-0688-6

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