Results 11 to 20 of about 4,027,933 (261)

Deciphering the Geometric Bonferroni Mean Operator in Pythagorean Neutrosophic Sets Framework [PDF]

open access: yesNeutrosophic Sets and Systems
The Geometric Bonferroni Mean (GBM), is an extension of The Bonferroni mean (BM), that combines both BM and the geometric mean, allowing for the representation of correlations among the combined factors while acknowledging the inherent uncertainty within
Mohammad Shafiq bin Mohammad Kamari   +5 more
doaj   +4 more sources

Pythagorean Fuzzy Interaction Partitioned Bonferroni Mean Operators and Their Application in Multiple-Attribute Decision-Making

open access: yesComplexity, 2018
The aim of this paper is to develop partitioned Pythagorean fuzzy interaction Bonferroni mean operators based on the Pythagorean fuzzy set, Bonferroni mean, and interaction between membership and nonmembership.
Wei Yang   +4 more
doaj   +5 more sources

Development of the Generalized Multi-Dimensional Extended Partitioned Bonferroni Mean Operator and Its Application in Hierarchical MCDM

open access: yesAxioms, 2022
In this article, we propose the generalized version of the extended, partitioned Bonferroni mean (EPBM) operator with a systematic investigation of its behavior and properties.
Debasmita Banerjee   +3 more
doaj   +4 more sources

Bonferroni Mean Operators of Linguistic Neutrosophic Numbers and Their Multiple Attribute Group Decision-Making Methods

open access: yesInformation, 2017
Linguistic neutrosophic numbers (LNN) is presented by Fang and Ye in 2017, which can describe the truth, falsity, and indeterminacy linguistic information independently.
Changxing Fan, Jun Ye, Keli Hu, En Fan
doaj   +4 more sources

T-Spherical Fuzzy Bonferroni Mean Operators and Their Application in Multiple Attribute Decision Making

open access: yesMathematics, 2022
To deal with complicated decision problems with T-Spherical fuzzy values in the aggregation process, T-Spherical fuzzy Bonferroni mean operators are developed by extending the Bonferroni mean and Dombi mean to a T-Spherical fuzzy environment.
Wei Yang, Yongfeng Pang
doaj   +2 more sources

Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications

open access: yesIEEE Access
In this paper, some T-spherical hesitant fuzzy Bonferroni mean aggregation operators have been developed by extending the Bonferroni mean to the T-spherical hesitant fuzzy environment. The attribute weights are calculated by using the Shapley function to
Yongfeng Pang, Wei Yang
doaj   +3 more sources

BONFERRONI MEANS WITH THE INDUCED OWAWA OPERATOR

open access: yesFUZZY ECONOMIC REVIEW, 2020
The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents a new operator that takes into the same formulation the IOWA operator and the Bonferroni means.
Leon-Castro E.   +2 more
core   +4 more sources

Dual Hesitant q-Rung Orthopair Fuzzy Interaction Partitioned Bonferroni Mean Operators and Their Applications

open access: yesJournal of Mathematics, 2023
The purpose of this paper is to introduce interaction partitioned Bonferroni mean operators under dual hesitant q-rung orthopair fuzzy environment. Motivated by the idea of q-rung orthopair fuzzy interaction operational laws, partitioned Bonferroni mean,
Lu Zhang, Yabin Shao, Ning Wang
doaj   +2 more sources

Multiattribute Group Decision Making Methods Based on Linguistic Intuitionistic Fuzzy Power Bonferroni Mean Operators [PDF]

open access: yesComplexity, 2017
This paper focuses on the multiattribute group decision making problems with linguistic intuitionistic fuzzy information. Firstly the concept of linguistic intuitionistic fuzzy numbers (LIFNs) is introduced, and then based on the LIFNs, some new ...
Peide Liu, Xi Liu
doaj   +2 more sources

Generalized Fuzzy Soft Power Bonferroni Mean Operators and Their Application in Decision Making [PDF]

open access: yesSymmetry, 2021
In decision-making process, decision-makers may make different decisions because of their different experiences and knowledge. The abnormal preference value given by the biased decision-maker (the value that is too large or too small in the original data) may affect the decision result.
Xu, Zitai, Chen, Chunfang, Yang, Yutao
openaire   +2 more sources

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