Fuzzy Optimization and Decision Making

, Volume 4, Issue 2, pp 119–130 | Cite as

Learning Weights in the Generalized OWA Operators



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.


aggregation operators ordered weighted averaging Choquet integral fuzzy sets 


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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.School of Information TechnologyDeakin UniversityBurwoodAustralia

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