循环直觉模糊的哈马切尔聚合运算符用于多属性决策.
Aliya Fahmi1, Aziz Khan2, Zahida Maqbool3
1Department of Mathematics and Statistics, The University of Faisalabad, Faisalabad, Pakistan. aliyafahmi@gmail.com.
Scientific reports
|February 15, 2025
概括
本研究为循环直觉模糊集 (C-IFS) 引入了新的 Hamacher 聚合运算符,以改善不确定环境中的决策. 新框架在复杂场景中提高了精度和效率.
科学领域:
- 决策科学 决策科学
- 计算智能是一种计算智能.
- 模糊的数学 模糊的数学
背景情况:
- 不确定和不精确的决策需要强大的数学框架来处理相互矛盾的信息.
- 循环直觉模糊集 (C-IFS) 通过整合直觉和立方模糊集属性提供了一种强大的方法.
- 现有的C-IFS方法需要增强的操作框架,以提高灵活性和适应性.
研究的目的:
- 通过将它们与Hamacher操作框架集成,扩大C-IFS的应用范围.
- 根据C-IFS的Hamacher操作框架引入六个新的聚合运营商.
- 开发一个计算框架,以提高准确性和效率,用于多标准决策.
主要方法:
- 拟议的六个新型聚合运营商:CIFHWA,CIFHOWA,CIFHHWA,CIFHWG,CIFHOWG和CIFHHWG.
- 开发了评分和准确性功能,用于对C-IFS进行排名.
- 实现了一个基于神经系统的方案,使用立方相关系数来提高计算效率.
主要成果:
- 拟议的哈马切尔运营商实际上将C-IFS集成用于决策.
- 评分和准确性功能提供了一种可靠的方法来排名C-IFS.
- 基于神经系统的方案展示了通过数值示例验证的计算效率和实际实用性.
- 对比分析证实了拟议框架在现有技术上的优越性.
结论:
- 哈马赫公司的业务与C-IFS的整合在模糊决策方面取得了重大进展.
- 新的聚合运算符和计算框架提高了处理复杂,不确定的数据的能力.
- 这项研究为模糊的决策和不确定的数据分析开辟了新的途径.
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