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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2017

03.09.2015 | Original Article

Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators

verfasst von: Peide Liu, Honggang Li

Erschienen in: Neural Computing and Applications | Ausgabe 1/2017

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Abstract

Normal neutrosophic numbers (NNNs) are a significant tool of describing the incompleteness, indeterminacy, and inconsistency of the decision-making information. In this paper, we firstly propose the definition and the properties of the NNNs, and the accuracy function, the score function, and the operational laws of the NNNs are developed. Then, some operators are presented, including the normal neutrosophic Bonferroni mean operator, the normal neutrosophic weighted Bonferroni mean (NNWBM) operator, the normal neutrosophic geometric Bonferroni mean operator, and the normal neutrosophic weighted geometric Bonferroni mean (NNWGBM) operator. We also study their properties and special cases. Further, we put forward a multiple attribute decision-making method which is based on the NNWBM and NNWGBM operators. Finally, an illustrative example is given to verify the practicality and validity of the proposed method.
Vorheriger Artikel An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem
Nächster Artikel Hesitant triangular multiplicative aggregation operators and their application to multiple attribute group decision making

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Metadaten
Titel
Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators
verfasst von
Peide Liu
Honggang Li
Publikationsdatum
03.09.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2048-z

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