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一般化 q-rung 图像语言 Schweizer 和 Sklar 聚合运算符及其在决策中的应用

Jawad Ali¹, Usman Khalid², Muhammad Ahsan Binyamin²

¹Department of Mathematics, Quaid-i-Azam University, 45320, Islamabad, Pakistan.

Scientific reports

|March 3, 2025

概括

本研究介绍了使用Schweizer-Sklar运算进行复杂数据分析的q-rung图像模糊集. 新的聚合运营商在多属性决策 (MADM) 问题中加强决策.

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

背景情况:

Q-rung图片模糊集扩展现有的模糊集理论,如q-rung整形对模糊集和图片模糊集.
与传统方法相比,参数q和三个成员函数可以更好地处理复杂和不确定的数据.

研究的目的:

在使用Schweizer-Sklar (SS) 运算的q-rung图像模糊语言集 (q-RPLFS) 框架内开发新的聚合运算符.
介绍和分析q-rung图片语言SS加权平均运算符 (q-RPLSSAO) 和q-rung图片语言SS几何运算符 (q-RPLSSGO).
应用q-RPLFS框架与SS t-规范和t-规范来解决多属性决策 (MADM) 问题,增强灵活性.

主要方法:

基于Schweizer-Sklar (SS) 运算的聚合运算符的开发,用于q-rung图像模糊语言集 (q-RPLFS) 框架.
具体运算符的介绍和详细分析:q-rung图片语言SS加权平均运算符 (q-RPLSSAO) 和q-rung图片语言SS几何运算符 (q-RPLSSGO).
应用q-RPLFS框架,包括SS t-规范和t-规范用于MADM问题解决.

主要成果:

拟议的q-RPLFS框架与SS操作为MADM问题提供了更灵活的信息聚合技术.
数字示例展示了开发的聚合运算符的适用性和优势.
与现有方法的比较证实了建议方法的准确性和可行性.

结论:

用瑞士-斯克拉尔运算增强的q-rung图像模糊的语言集,为处理复杂数据提供了强大的扩展.
开发的聚合运算符为解决多属性决策问题提供了灵活而准确的方法.
该研究通过数值示例和比较分析验证了拟议框架的有效性.

关键词:

这就是夫人,夫人.电力聚合运营商是电力聚合运营商.Q-rung整形夫妇模糊的套件 Q-rung图片语言模糊的集合.瑞士和斯克拉尔 (SS) 标准.

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