基于爱因斯坦在q-球形模糊粗略集下的平均值的聚合运算符及其在自动汽车导航系统中的应用
Ahmad Bin Azim1, Asad Ali1, Abdul Samad Khan2
1Department of Mathematics and Statistics, Hazara University Mansehra, 21300, Khyber Pakhtunkhwa, Pakistan.
Heliyon
|August 15, 2024
概括
这项研究介绍了新的爱因斯坦运算和聚合算法,用于q-球形模糊粗数据. 这些新方法提高了对复杂,不确定的信息的决策准确性.
科学领域:
- 数学 数学 是一个数学.
- 计算机科学 计算机科学
- 决策科学 决策科学 决策科学
背景情况:
- 处理复杂和不确定的数据是决策中的一个重大挑战.
- 现有的聚合运营商可能缺乏对细微数据类型所需的精度.
- Q-球体模糊粗略集为表示复杂不确定性的框架提供了一个框架.
研究的目的:
- 为q-球体模糊粗数据引入创新的操作规律和聚合算法.
- 开发和介绍三个新的爱因斯坦平均运算符:加权,有序加权和混合加权平均.
- 为了证明这些操作员在属性决策场景中的适用性和有效性.
主要方法:
- 开发基于爱因斯坦运算的新运算定律,用于q-球形模糊粗体集合.
- 设计了三个新的聚合运算符:q-球形模糊粗的爱因斯坦加权平均,有序加权平均和混合加权平均.
- 在使用q-spherical模糊粗略数据的属性决策问题中实施和验证拟议的运算符.
主要成果:
- 提出的爱因斯坦平均运算符提高了对q-球形模糊粗数据的算术平均的精度和准确性.
- 该研究表明了这些操作员在属性决策中的实际实施和有效性.
- 对比和敏感性分析证实了拟议方法的稳定性和优势,相对于现有方法.
结论:
- 新开发的运算符和算法为复杂和不确定的q-spherical模糊粗数据提供了有效的决策工具.
- 该研究贡献了新的方法,丰富了模糊的粗略集合理论的理解和应用.
- 这些发现突出了在各种含糊不清的决策过程中具有重大实际应用的潜力.
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