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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 10/2019

10.01.2019 | Original Article

Geometric aggregation operators with interval-valued Pythagorean trapezoidal fuzzy numbers based on Einstein operations and their application in group decision making

verfasst von: M. Shakeel, S. Abdullah, M. Shahzad, Nasir Siddiqui

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 10/2019

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Abstract

The aim of this paper is to investigate information aggregation methods under interval-valued Pythagorean trapezoidal fuzzy environment. Some Einstein operational laws on interval-valued Pythagorean trapezoidal fuzzy numbers are defined based on Einstein sum and Einstein product. Based on Einstein operations, we define interval-valued Pythagorean trapezoidal fuzzy aggregation operators, such as interval-valued Pythagorean trapezoidal fuzzy Einstein weighted geometric operator, interval-valued Pythagorean trapezoidal fuzzy Einstein ordered weighted geometric operator and interval-valued Pythagorean trapezoidal fuzzy Einstein hybrid geometric operator. Furthermore, we apply the proposed aggregation operators to deal with multiple attribute group decision making problem. Finally we construct a numerical example for multiple attribute group decision making problem and compare the result with existing methods.
Vorheriger Artikel Adaptive sliding-mode observer for second order discrete-time MIMO nonlinear systems based on recurrent neural-networks
Nächster Artikel Recommendations based on user effective point-of-interest path

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Metadaten
Titel
Geometric aggregation operators with interval-valued Pythagorean trapezoidal fuzzy numbers based on Einstein operations and their application in group decision making
verfasst von
M. Shakeel
S. Abdullah
M. Shahzad
Nasir Siddiqui
Publikationsdatum
10.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 10/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-00909-y

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