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一个强大的E学习推系统基于新的间隔估值双极模糊超软集合理论
Muhammad Imran Harl1, Muhammad Saeed1, Muhammad Haris Saeed2
1Department of Mathematics, University of Management and Technology, Lahore, 54700, Punjab, Pakistan.
Scientific reports
|March 13, 2026
概括
这项研究引入了一种新的间隔值双极模糊超软集 (IVBFHS),用于使用双极信息进行决策. 新框架通过有效处理复杂的基于间隔的数据来增强多属性决策 (MADM).
科学领域:
- 模糊的集合理论 模糊的集合理论
- 决策支持系统是什么?
- 多属性决策 (MADM) 是一种多属性决策.
背景情况:
- 双极信息对于平衡的决策至关重要,需要处理不确定性的工具.
- 间隔值双极模糊集 (IVBFS) 提供了一种捕获间隔值双极信息的方法.
- 双极超软集 (BHSS) 为多属性分析提供了一个框架,直至子属性级别.
研究的目的:
- 为了引入混合数据结构,介绍了区间值双极模糊超软集 (IVBFHS).
- 合并IVBFS和BHSS的处理双极,基于间隔的多属性数据的能力.
- 使用拟议的IVBFHS框架为MADM问题开发决策支持算法.
主要方法:
- 间隔值双极模糊超软集 (IVBFHS) 数据结构的开发.
- 分析IVBFHS的基本运算和属性 (交换性,关联性,分配性,德摩根定律).
- 创建一个优先决策支持算法,用于在电子学习场景中选择最佳替代方案.
主要成果:
- 拟议的IVBFHS有效地操纵和处理跨多个属性和子属性间隔呈现的双极信息.
- 定义和分析了IVBFHS的基本特征和操作,确保其数学稳定性.
- 基于IVBFHS的决策支持算法在MADM问题中展示了适应性和可靠性,通过计算和结构比较进行验证.
结论:
- 间隔值双极模糊超软集 (IVBFHS) 是一个强大的扩展,用于处理复杂的决策场景与双极,间隔值和多属性数据.
- 开发的决策支持算法为合理的决策提供了一个系统的方法,特别适用于电子学习等领域.
- 该研究验证了建议框架对高级决策支持应用程序的有效性和可靠性.
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