Results 161 to 170 of about 9,517 (208)
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On the Comparisons of OWA Operators and Ordinal OWA Operators
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1997In this paper, comparisons of OWA operators and ordinal OWA operators are carried out by using lattice theoretic methods. It is proved first that in both cases, the set of all aggregation operators forms a lattice, then the concept of positive valuation is used to measure the "orness" of aggregation operators and the structures of all such possible ...
Taihe Fan, Dan A. Ralescu
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International Journal of Approximate Reasoning, 2023
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Vikas Srivastava, Amit K. Singh
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Vikas Srivastava, Amit K. Singh
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Two new models for determining OWA operator weights
Computers and Industrial Engineering, 2007 The determination of ordered weighted averaging (OWA) operator weights is a very crucial issue of applying the OWA operator for decision making. This paper proposes two new models for determining the OWA operator weights.
Xinwang Liu
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IEEE Transactions on Fuzzy Systems, 2015
A critical issue when selecting an ordered weighted aggregation (OWA) operator is the determination of the associated weights. For this reason, numerous weight generating methods have appeared in the literature. In this paper, a generalization of the binomial OWA operator on the basis of the Stancu polynomial is proposed and analyzed.
Amit K. Singh +2 more
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A critical issue when selecting an ordered weighted aggregation (OWA) operator is the determination of the associated weights. For this reason, numerous weight generating methods have appeared in the literature. In this paper, a generalization of the binomial OWA operator on the basis of the Stancu polynomial is proposed and analyzed.
Amit K. Singh +2 more
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International Journal of Intelligent Systems, 2002
Summary: The Ordered Weighted Averaging (OWA) operator was introduced by Yager to provide a method for aggregating several inputs that lie between the max and min operators. In this article, we investigate the uncertain OWA operator in which the associated weighting parameters cannot be specified, but value ranges can be obtained and each input ...
Zeshui Xu, Qingli Da
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Summary: The Ordered Weighted Averaging (OWA) operator was introduced by Yager to provide a method for aggregating several inputs that lie between the max and min operators. In this article, we investigate the uncertain OWA operator in which the associated weighting parameters cannot be specified, but value ranges can be obtained and each input ...
Zeshui Xu, Qingli Da
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Generalizations of OWA Operators
IEEE Transactions on Fuzzy Systems, 2015OWA operators can be seen as symmetrized weighted arithmetic means, as Choquet integrals with respect to symmetric measures, or as comonotone additive functionals. Following these three different looks on OWAs, we discuss several already known generalizations of OWA operators, including GOWA, IOWA, OMA operators, as well as we propose new types of such
Radko Mesiar +2 more
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Mm-OWA: A Generalization of OWA Operators
IEEE Transactions on Fuzzy Systems, 2018We characterize those operators that satisfy the properties of monotonicity, permutation invariance, positive homogeneity, and translation invariance. As these operators do not necessarily satisfy comonotonic additivity, their class is larger than that of ordered weighted averaging (OWA) operators.
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International Journal of Intelligent Systems, 1999
Summary: The Ordered Weighting Averaging (OWA) Operator of Yager was introduced to provide a method for nonlinearly aggregating a set of input arguments \(a_i\). A fundamental aspect of the OWA operator is a reordering step in which the input arguments are rearranged according to their values. Recently, a generalized OWA operator was described in which
Paul A. Schaefer, H. B. Mitchell
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Summary: The Ordered Weighting Averaging (OWA) Operator of Yager was introduced to provide a method for nonlinearly aggregating a set of input arguments \(a_i\). A fundamental aspect of the OWA operator is a reordering step in which the input arguments are rearranged according to their values. Recently, a generalized OWA operator was described in which
Paul A. Schaefer, H. B. Mitchell
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IEEE Transactions on Fuzzy Systems, 2018
Inspired by the real needs of group decision problems, aggregation of ordered weighted averaging (OWA) operators is studied and discussed. Our results can be applied for data acting on any real interval, such as the standard scales $[0,1]$ and $[0,\infty [$ , bipolar scales $[-1,1]$ and $\mathbb {R}=]-\infty, \infty [$ , etc.
Radko Mesiar +3 more
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Inspired by the real needs of group decision problems, aggregation of ordered weighted averaging (OWA) operators is studied and discussed. Our results can be applied for data acting on any real interval, such as the standard scales $[0,1]$ and $[0,\infty [$ , bipolar scales $[-1,1]$ and $\mathbb {R}=]-\infty, \infty [$ , etc.
Radko Mesiar +3 more
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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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