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Confidence-guided cryo-EM map optimisation with LocScale-2.0
Bharadwaj A, de Bruin R, Jakobi AJ.
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Bipolar ordered weighted averages: BIOWA operators
Fuzzy Sets and Systems, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mesiar, Radko +2 more
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Ordered fuzzy weighted averages and ordered linguistic weighted averages
International Conference on Fuzzy Systems, 2010The ordered weighted average (OWA) operator has been widely used in decision-making. In many situations, however, providing crisp numbers for either the sub-criteria or the weights is problematic (there could be uncertainties about them), and it is more meaningful to provide intervals, type-1 fuzzy sets (T1 FSs), interval type-2 fuzzy sets (IT2 FSs ...
Dongrui Wu, Jerry M. Mendel
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Induced ordered weighted averaging operators
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999We briefly describe the Ordered Weighted Averaging (OWA) operator and discuss a methodology for learning the associated weighting vector from observational data. We then introduce a more general type of OWA operator called the Induced Ordered Weighted Averaging (IOWA) Operator.
R R, Yager, D P, Filev
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Least-squared ordered weighted averaging operator weights
International Journal of Intelligent Systems, 2007The ordered weighted averaging (OWA) operator by Yager (IEEE Trans Syst Man Cybern 1988; 18; 183–190) has received much more attention since its appearance. One key point in the OWA operator is to determine its associated weights. Among numerous methods that have appeared in the literature, we notice the maximum entropy OWA (MEOWA) weights that are ...
Byeong Seok Ahn, Haechurl Park
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2015
The focus of this chapter is on OWA functions. The formal definitions and the main properties of OWA are presented. Some extensions of the OWA functions are discussed in detail. Various methods of fitting OWA functions to empirical data are presented.
Gleb Beliakov +2 more
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The focus of this chapter is on OWA functions. The formal definitions and the main properties of OWA are presented. Some extensions of the OWA functions are discussed in detail. Various methods of fitting OWA functions to empirical data are presented.
Gleb Beliakov +2 more
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SIMILARITY CLASSIFIER WITH WEIGHTED ORDERED WEIGHTED AVERAGING OPERATOR
FUZZY ECONOMIC REVIEW, 2016(ProQuest: ... denotes formulae omitted.)1.INTRODUCTIONVast amount of data produced by computers and the internet of things and the increased ability to create automated systems have led to a growing need for effective ways to monitor performance and to detect problems, monitoring and problem detection are often linked to classification as states of ...
O. Kurama, P. Luukka, M. Collan
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