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Normal neutrosophic frank aggregation operators and their application in multi-attribute group decision making

机译:正常中智弗兰克聚集算子及其在多属性群决策中的应用

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Normal neutrosophic set (NNS) can conveniently express random and fuzzy information, and Frank operators have the properties of generalization and flexibility. In this paper, we will extend Frank operators to Normal neutrosophic numbers (NNNs), and propose some Frank aggregation Operators for NNNs, then develop two new decision methods with NNNs. Firstly, based on Frank operators, the operational laws of NNNs are redefined, and their operational properties are proved, then the normal neutrosophic Frank averaging operator (NNFWA) and normal neutrosophic Frank weighted geometric operator (NNFWG) are developed. Further, some desirable characteristics, such as idempotency, boundedness and commutativity, are discussed in detail, and some special cases are studied. Furthermore, to deal with the multiple attribute group decision making (MAGDM) problems in which attribute values take the form of NNNs, two methods on the basis of NNFWA and NNFWG operators are developed, and they are more general and more flexible by Frank operations. Finally, an example is given to illustrate the proposed methods and demonstrate their practicality and availability.
机译:正常中智集(NNS)可以方便地表示随机和模糊信息,Frank算子具有泛化和灵活的特性。在本文中,我们将把Frank算子扩展到正常中性数(NNNs),并提出一些用于NNN的Frank算子,然后用NNNs开发两种新的决策方法。首先,基于弗兰克算子,重新定义了神经网络的运行规律,证明了它们的运行特性,然后发展了正常中智弗兰克平均算子(NNFWA)和正常中智弗兰克加权几何算子(NNFWG)。此外,详细讨论了一些理想的特性,例如幂等性,有界性和可交换性,并研究了一些特殊情况。此外,为了解决属性值采用NNN形式的多属性组决策(MAGDM)问题,开发了两种基于NNFWA和NNFWG运算符的方法,通过Frank运算,它们变得更通用,更灵活。最后,给出一个例子来说明所提出的方法并证明其实用性和可用性。

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