Identification of conservation priorities in the major basins of Central Asia: Using an integrated GIS-based ordered weighted averaging approach

2021 ◽  
Vol 298 ◽  
pp. 113442
Author(s):  
Jiangyue Li ◽  
Xi Chen ◽  
Alishir Kurban ◽  
Tim Van de Voorde ◽  
Philippe De Maeyer ◽  
...  
Keyword(s):  
Central Asia ◽  
Weighted Averaging ◽  
Conservation Priorities ◽  
Ordered Weighted Averaging

scholarly journals A Study of OWA Operators Learned in Convolutional Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Practical Method ◽  
Local Information ◽  
Weighted Averaging ◽  
Global Information ◽  
Owa Operator ◽  
Owa Operators ◽  
Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

Using Induced Ordered Weighted Averaging (IOWA) Operators for Aggregation in Cross-Efficiency Evaluations

2014 ◽  
Vol 29 (12) ◽  
pp. 1100-1116 ◽  
Author(s):  
Teresa León ◽  
Nuria Ramón ◽  
José L. Ruiz ◽  
Inmaculada Sirvent
Keyword(s):  
Weighted Averaging ◽  
Ordered Weighted Averaging

scholarly journals Triangular Cubic Hesitant Fuzzy Einstein Hybrid Weighted Averaging Operator and Its Application to Decision Making

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 658 ◽  
Author(s):  
Aliya Fahmi ◽  
Fazli Amin ◽  
Florentin Smarandache ◽  
Madad Khan ◽  
Nasruddin Hassan
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Linguistic Information ◽  
Ordered Weighted Averaging ◽  
Averaging Operator ◽  
Multiple Attribute ◽  
The Relationship

In this paper, triangular cubic hesitant fuzzy Einstein weighted averaging (TCHFEWA) operator, triangular cubic hesitant fuzzy Einstein ordered weighted averaging (TCHFEOWA) operator and triangular cubic hesitant fuzzy Einstein hybrid weighted averaging (TCHFEHWA) operator are proposed. An approach to multiple attribute group decision making with linguistic information is developed based on the TCHFEWA and the TCHFEHWA operators. Furthermore, we establish various properties of these operators and derive the relationship between the proposed operators and the existing aggregation operators. Finally, a numerical example is provided to demonstrate the application of the established approach.

scholarly journals Some Generalized Single Valued Neutrosophic Linguistic Operators and Their Application to Multiple Attribute Group Decision Making

2017 ◽  
Vol 5 (2) ◽  
pp. 148-162 ◽  
Author(s):  
Ruipu Tan ◽  
Wende Zhang ◽  
Shengqun Chen
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Group Decision Making ◽  
Group Decision ◽  
Weighted Averaging ◽  
Attribute Weights ◽  
Ordered Weighted Averaging ◽  
Aggregate Information ◽  
Multiple Attribute

Abstract This paper proposes a group decision making method based on entropy of neutrosophic linguistic sets and generalized single valued neutrosophic linguistic operators. This method is applied to solve the multiple attribute group decision making problems under single valued neutrosophic liguistic environment, in which the attribute weights are completely unknown. First, the attribute weights are obtained by using the entropy of neutrosophic linguistic sets. Then three generalized single valued neutrosophic linguistic operators are introduced, including the generalized single valued neutrosophic linguistic weighted averaging (GSVNLWA) operator, the generalized single valued neutrosophic linguistic ordered weighted averaging (GSVNLOWA) operator and the generalized single valued neutrosophic linguistic hybrid averaging (GSVNLHA) operator, and the GSVNLWA and GSVNLHA operators are used to aggregate information. Furthermore, similarity measure based on single valued neutrosophic linguistic numbers is defined and used to sort the alternatives and obtain the best alternative. Finally, an illustrative example is given to demonstrate the feasibility and effectiveness of the developed method.

scholarly journals Induced intuitionistic fuzzy ordered weighted averaging: Weighted average operator and its application to business decision-making

2014 ◽  
Vol 11 (2) ◽  
pp. 839-857 ◽  
Author(s):  
Zeng Shouzhen ◽  
Wang Qifeng ◽  
José Merigó ◽  
Pan Tiejun
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Aggregation Operator ◽  
Weighted Averaging ◽  
Business Decision ◽  
Ordered Weighted Averaging ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Weighted Average ◽  
Decision Making Problem ◽  
Selection Of

We present the induced intuitionistic fuzzy ordered weighted averaging-weighted average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy weighted average (IFWA) and the induced intuitionistic fuzzy ordered weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.

scholarly journals Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Lipeng Pan ◽  
Yong Deng
Keyword(s):  
Probability Distribution ◽  
Evidence Theory ◽  
Mass Function ◽  
New Method ◽  
Weighted Averaging ◽  
Minimum Entropy ◽  
Transform Method ◽  
Numerical Examples ◽  
Ordered Weighted Averaging ◽  
Open Issue

Dempster-Shafer evidence theory can handle imprecise and unknown information, which has attracted many people. In most cases, the mass function can be translated into the probability, which is useful to expand the applications of the D-S evidence theory. However, how to reasonably transfer the mass function to the probability distribution is still an open issue. Hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. The new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. Then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. Finally, some numerical examples are given to prove that new method is more reasonable and effective.

scholarly journals A method for decision making with the OWA operator

2012 ◽  
Vol 9 (1) ◽  
pp. 357-380 ◽  
Author(s):  
José Merigó ◽  
Anna Gil-Lafuente
Keyword(s):  
Decision Making ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Minimum Level ◽  
Ordered Weighted Averaging ◽  
Special Cases ◽  
Wide Range ◽  
Decision Making Problem ◽  
Parameterized Family

A new method for decision making that uses the ordered weighted averaging (OWA) operator in the aggregation of the information is presented. It is used a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index is based on distance measures and other techniques that are useful for decision making. By using the OWA operator in the IMAM, we form a new aggregation operator that we call the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. The main advantage is that it provides a parameterized family of aggregation operators between the minimum and the maximum and a wide range of special cases. Then, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. A further extension of this approach is presented by using hybrid averages and Choquet integrals. We also develop an application of the new approach in a multi-person decision-making problem regarding the selection of strategies.

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