基于邻域系统的一些隶属函数在粗糙集决策中的应用

IF 0.4 Q4 MATHEMATICS
A. E. F. El Atik, A. Zedan
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引用次数: 0

摘要

邻域结构可以表示关于宇宙物体之间关系的信息或知识。换句话说,这样的元素或对象在某种程度上类似于元素邻域中的元素。Pawlak提出了将粗糙集作为学习计算机科学和信息系统的有用工具的想法。邻域结构利用这一原理进行了推广和研究。本文用邻域方法解决了几个粗糙集理论问题。利用信息系统中对象的邻域及其应用实例,介绍了属性、隶属函数和精度度量的一些新定义。我们方法的决策提供了准确的决策,并有助于通过决策相关性来计算每个属性的准确性,从而构建决策方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some membership functions via neighborhood systems: application to a rough set decision making
The neighborhood structure can represent information or knowledge about relationships between a universe's object. In other words, such elements or objects are somewhat  similar  to that element in an element's neighborhood.  Pawlak presented the idea of rough sets as useful tools for learning computer science and information systems. Neighborhood structures used this principle to be generalized and studied. This paper uses a neighborhood method to solve several rough set theory problems.  By using a neighborhood of objects in the information system and illustrative examples to apply it, we introduce some new definitions of attributes, membership function and accuracy measurement.  A decision making of our method gives an accurate decision and helps with decision correlation to calculate the accuracy of each attribute that builds an approach to decision making.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
140
审稿时长
25 weeks
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