爱因斯坦对球形模糊集合和聚合运算符的指数运算定律在决策过程中
D Ajay1, Ganeshsree Selvachandran2,3, J Aldring1,4
1Department of Mathematics, Sacred Heart College, Tamilnadu, India.
概括
本研究引入了球形模糊集 (SFS) 的新操作规律,以处理决策中的不确定性. 提出了新的聚合运算符和MCDM算法来对复杂的选择进行排名.
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 决策科学
- 人工智能的人工智能
背景情况:
- 球形模糊集 (SFS) 扩展模糊集 (FS) 来管理决策中的模糊性.
- 现有的SFS运营法律在处理复杂的不确定性方面存在局限性.
- 需要先进的聚合运营商来改善基于SFS的决策支持.
研究的目的:
- 定义新的指数和爱因斯坦指数操作定律用于球形模糊集合.
- 引入新的聚合运算符,球体模糊加权指数平均 (SFWEA) 和球体模糊爱因斯坦加权指数平均 (SFEWEA).
- 开发和应用一个使用这些运算符的多标准决策 (MCDM) 算法.
主要方法:
- 对SFS的指数和爱因斯坦指数运算定律的定义,具有鲜明的基础和球形模糊指数.
- 基于新的运营法律的SFWEA和SFEWEA聚合运营商的发展.
- 拟议的MCDM算法的应用来对心理治疗类型进行排名.
主要成果:
- 成功为SFS定义了新的运营法律和聚合运营商.
- 证明了拟议的SFWEA和SFEWEA运营商的实用性.
- 该MCDM算法有效地对心理治疗选择进行了排名,展示了其实际适用性.
结论:
- 新的运营法律和聚合运营商提高了SFS在不确定性下决策的能力.
- 拟议的SFWEA和SFEWEA运营商为复杂的聚合任务提供了一个强大的框架.
- 开发的MCDM算法为实际决策问题提供了有价值的工具,例如心理治疗选择.
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