基于费尔马特模糊集合的距离测量和知识测量的多标准决策
1School of Mathematics, Faculty of Sciences, SMVD University, Katra, J&K 182320 India.
概括
本研究为费尔马特模糊集合 (FFS) 引入了新的距离和知识测量方法,提高了决策中的不确定性处理. 这些新的措施比在毕达哥拉斯/费尔马特模糊设置中的现有措施更有效.
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
- 信息理论 信息理论
背景情况:
- 模糊集,直觉模糊集,和毕达哥拉模糊集解决不确定性.
- 距离和知识测量在模式分析,集群,医学诊断和多标准决策中至关重要.
- 现有的研究缺乏专门针对费尔马特模糊集合的距离和知识指标.
研究的目的:
- 提出使用t-conorms的费尔马特模糊集合的新型距离测量方法.
- 根据拟议的距离测量,为费尔马特模糊集合引入新的知识测量.
- 评估拟议措施的有效性和适用性.
主要方法:
- 为费尔马特模糊群开发新的距离测量方法.
- 从拟议的距离尺度中得出的知识尺度的制定.
- 对属性的分析和与现有措施进行数值比较.
- 在模式分析和多标准决策中的应用演示.
主要成果:
- 对于费尔马特模糊集合的新型距离和知识测量方法已成功提出.
- 拟议的措施在毕达哥拉斯/费尔马特模糊背景下,与现有措施相比,表现出更高的有效性.
- 新措施的实用性通过数值示例和语言对冲来验证.
结论:
- 开发的距离和知识测量提供了在费尔马特模糊集框架内处理不确定性的增强能力.
- 这些措施为模式分析和多标准决策问题提供了有价值的工具.
- 这项研究填补了关于费尔马特模糊集测量的现有文献中的关键空白.
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