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Learning weights in the generalized OWA operators

Beliakov, Gleb 2005, Learning weights in the generalized OWA operators, Fuzzy optimization and decision making, vol. 4, no. 2, pp. 119-130.

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Title Learning weights in the generalized OWA operators
Author(s) Beliakov, Gleb
Journal name Fuzzy optimization and decision making
Volume number 4
Issue number 2
Start page 119
End page 130
Publisher Kluwer Academic Publishers
Place of publication Dordrecht, Netherlands
Publication date 2005-04
ISSN 1568-4539
Keyword(s) aggregation operators
ordered weighted averaging
choquet integral
fuzzy sets
Summary This paper discusses identification of parameters of generalized ordered weighted averaging (GOWA) operators from empirical data. Similarly to ordinary OWA operators, GOWA are characterized by a vector of weights, as well as the power to which the arguments are raised. We develop optimization techniques which allow one to fit such operators to the observed data. We also generalize these methods for functional defined GOWA and generalized Choquet integral based aggregation operators.

Notes The original publication can be found at www.springerlink.com
Language eng
Field of Research 010201 Approximation Theory and Asymptotic Methods
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003042

Document type: Journal Article
Collections: School of Information Technology
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