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AN INTUITIONISTIC OWA OPERATOR
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004The OWA (Ordered Weighted Average) operator is a powerful non-linear operator for aggregating a set of inputs ai,i∈{1,2,…,M}. In the original OWA operator the inputs are crisp variables ai. This restriction was subsequently removed by Mitchell and Schaefer who by application of the extension principle defined a fuzzy OWA operator which aggregates a ...
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Fuzzy Optimization and Decision Making, 2002
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International Journal of Intelligent Systems, 1997
Summary: One of the properties that the OWA operator satisfies is commutativity. This condition, that is not satisfied by the weighted mean, stands for equal reliability of all the information sources that supply the data. In this article we define a new combination function, the WOWA (weighted OWA), that combines the advantages of the OWA operator and
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Summary: One of the properties that the OWA operator satisfies is commutativity. This condition, that is not satisfied by the weighted mean, stands for equal reliability of all the information sources that supply the data. In this article we define a new combination function, the WOWA (weighted OWA), that combines the advantages of the OWA operator and
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Fuzzy Sets and Systems, 1996
Abstract The ordered weighted averaging operators are introduced and some of their properties described. Attention is then focused on the problem of maximizing an OWA aggregation of a group of variables that are interrelated and constrained by a collection of linear inequalities.
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Abstract The ordered weighted averaging operators are introduced and some of their properties described. Attention is then focused on the problem of maximizing an OWA aggregation of a group of variables that are interrelated and constrained by a collection of linear inequalities.
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Probabilities in the OWA operator
Expert Systems with Applications, 2012We analyze the use of the probability in the ordered weighted average (OWA). We introduce the probabilistic OWA (POWA) operator. It is an aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum that considers the degree of importance that the probability and the OWA operator have in the ...
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Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1999
The basic properties of the Ordered Weighted Averaging (OWA) operator are recalled. The role of these operators in the formulation of multi-criteria decision functions, using the concept of quantifier guided aggregation, is discussed. An extended class of OWA operators, one based upon a relaxation of the requirements on the OWA operators, is introduced.
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The basic properties of the Ordered Weighted Averaging (OWA) operator are recalled. The role of these operators in the formulation of multi-criteria decision functions, using the concept of quantifier guided aggregation, is discussed. An extended class of OWA operators, one based upon a relaxation of the requirements on the OWA operators, is introduced.
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OWA operators with functional weights
Fuzzy Sets and Systems, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jesús Medina 0001, Ronald R. Yager
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Sensitivity Analysis of the OWA Operator
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for ...
Mahdi Zarghami +2 more
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Soft Computing, 2006
We introduce the idea of centered OWA operators. We define these as OWA operators that give preference to argument values that lie in the middle between the largest and the smallest. An important class of these using Gaussian type weights is investigated in considerable detail. We describe a number of different examples of centered OWA operators.
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We introduce the idea of centered OWA operators. We define these as OWA operators that give preference to argument values that lie in the middle between the largest and the smallest. An important class of these using Gaussian type weights is investigated in considerable detail. We describe a number of different examples of centered OWA operators.
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RECURSIVE AND ITERATIVE OWA OPERATORS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2005An important issue when using the OWA aggregation operators is the determination of weights. One approach is to link the weights to a desired attitudinal character for the aggregation. The ME-OWA operators provide a pioneering example of this approach.
Luigi Troiano, Ronald R. Yager
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