爱因斯坦聚合运算符用于在不确定的环境中使用立方图片模糊集的多标准组决策
Muhammad Naeem Khan Tanoli1, Khadija Rafique1, Zafar Mahmood2
1Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
Scientific reports
|October 21, 2025
概括
这项研究引入了形图片模糊数据的新爱因斯坦平均运算符,提高了计算的精度. 这些运营商为复杂数据的决策提供了改进的工具,通过现实生活中的例子来验证.
科学领域:
- 模糊的集合理论 模糊的集合理论
- 决策科学 决策科学
- 信息融合 信息融合
背景情况:
- 在现代决策中,处理复杂和不确定的数据至关重要.
- 现有的聚合运营商可能缺乏对立方图像模糊环境所需的精度.
- 需要先进的工具来管理和分析多维模糊信息.
研究的目的:
- 为立方图片模糊数据开发和研究新的算术平均运算符.
- 为了引入立方图片模糊的爱因斯坦加权平均 (CPFEWA),爱因斯坦有序加权平均 (CPFEOWA) 和爱因斯坦混合加权平均 (CPFEHWA) 运算符.
- 为了证明这些运算符在多重属性决策 (MADM) 场景中的适用性.
主要方法:
- 开发了三种新的爱因斯坦平均运算符:CPFEWA,CPFEOWA和CPFEHWA.
- 对操作器属性的分析,包括idempotency,单调性和边界性.
- 在使用立方图片模糊数据的多属性决策 (MADM) 框架中应用CPFEHWA运算符.
主要成果:
- 拟议的CPFEWA,CPFEOWA和CPFEHWA运算符为立方图片模糊数据提供精确的算术平均值.
- CPFEHWA 运营商被证明是 CPFEWA 和 CPFEOWA 的通用扩展.
- 在现实生活中的MADM环境中通过数值示例证明CPFEHWA运营商的有效性.
结论:
- 新的爱因斯坦运算符在处理立方图片模糊数据方面取得了重大进展.
- 这些运营商为涉及复杂和不确定的信息的决策过程提供了有价值的工具.
- 该研究为收集和解释立方图片模糊数据提供了创新方法,增强了未来的研究和实际应用.
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