图像模糊集和基于未知度的权重的Aczel Alsina聚合运算符的决策算法
Abrar Hussain1, Yu Liu2, Kifayat Ullah1
1Department of Mathematics, Riphah International University (Lahore Campus), 54000, Lahore, Pakistan.
Heliyon
|March 22, 2024
概括
本研究引入了新的图像模糊集的聚合运算符,以模糊信息增强决策. 这些新的方法通过强有力的处理不准确的人类意见,改善了多属性组决策.
科学领域:
- 模糊的数学 模糊的数学
- 决策科学 决策科学 决策科学
- 信息融合 信息融合
背景情况:
- 聚合运营商 (AO) 对于处理不准确信息至关重要.
- 亚齐尔·阿尔西纳 (Aczel Alsina) 的聚合表达式在模糊的框架内扩展三角规范.
- 电力运营商平滑近似并减轻数据的影响.
研究的目的:
- 为了探索图片模糊集 (PFSs) 和Aczel Alsina聚合工具.
- 在图片模糊环境中开发强大的聚合操作.
- 为PFS引入新的Aczel Alsina电力聚合运营商.
主要方法:
- 已确立的图片 模糊的 Aczel Alsina 权力加权平均 (PFAAPWA) 和有序加权平均 (PFAAPOWA) 运营商.
- 开发了图片模糊的Aczel Alsina功率加权几何 (PFAAPWG) 和有序加权几何 (PFAAPOWG) 操作员.
- 为多属性组决策 (MAGDM) 问题设计了一个算法.
主要成果:
- 拟议的方法有效地汇总复杂的信息,并定义相互关系.
- 开发的算法解决了人类意见中的冗余和模糊信息.
- 实验案例研究和比较分析证明了这些方法的一致性和有效性.
结论:
- 对于PFS的新型Aczel Alsina聚合运营商为决策提供了强大的解决方案.
- 开发的MAGDM方法为处理不确定性提供了一个可靠的框架.
- 该研究为分析模糊环境中的复杂数据提供了先进的工具.
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