基于图像模糊集的分区双麦克劳林对称平均运算符及其在多属性决策问题中的应用
Tahir Mahmood1, Ubaid Ur Rehman2, Walid Emam3
1Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan. tahirbakhat@iiu.edu.pk.
Scientific reports
|November 27, 2023
概括
本研究为多属性决策 (MADM) 问题引入了新的图像模糊分区双麦克劳林对称平均值 (PDMSM) 运算符. 这些运营商有效地处理复杂的决策场景,并提供模糊的信息.
科学领域:
- 决策科学 决策科学
- 模糊的数学 模糊的数学
- 信息理论 信息理论
背景情况:
- 多属性决策 (MADM) 对于复杂的选择至关重要.
- 图像模糊数 (PFN) 为不确定性提供了一个强大的框架.
- 现有的MADM方法缺乏PFN的操作员.
研究的目的:
- 开发新的图片模糊分区双麦克劳林对称平均值 (PDMSM) 运算符.
- 调查这些新运算符的特性 (无能性,单调性,局限性).
- 应用开发的运算符来解决 PFN 的 MADM 问题.
主要方法:
- 将PDMSM操作员扩展到图片模糊环境中.
- 重量图片模糊PDMSM运算符的表述.
- 对基本运算符属性的分析.
- 开发一个使用新运算符的MADM程序.
主要成果:
- 图片模糊PDMSM和加权图片模糊PDMSM操作员的介绍.
- 展示一些关键的属性,如无能性,单调性和局限性.
- 在与PFN进行比较的MADM示例中成功应用.
结论:
- 拟议的图像模糊的PDMSM运营商为涉及PFN的MADM问题提供了一个强大的工具.
- 开发的方法提供了一个可靠的方法,在不确定性下做出决策.
- 该研究通过结合PFN和新型运营商来增强现有的MADM技术.
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