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Learning weights in the generalized OWA operators

journal contribution
posted on 2005-04-01, 00:00 authored by Gleb BeliakovGleb Beliakov
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.

History

Journal

Fuzzy optimization and decision making

Volume

4

Issue

2

Pagination

119 - 130

Publisher

Kluwer Academic Publishers

Location

Dordrecht, Netherlands

ISSN

1568-4539

Language

eng

Notes

The original publication can be found at www.springerlink.com

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2005, Springer

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