在犹不决的模糊语言环境下,使用证据理论做出三方小组决策
Wenke Ding1,2, Xingchen Li3, Xiajiong Shen1,2
1School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.
Scientific reports
|December 20, 2023
概括
这项研究为犹不决的模糊语言环境引入了一种新的三向决策模型. 它通过精细融合专家的信心和考虑更好的分类和推系统的成本来增强集团决策.
科学领域:
- 决策分析 决策分析
- 模糊的逻辑 模糊的逻辑
- 人工智能的人工智能
背景情况:
- 决策者经常因为犹不决的态度而难以进行数值评估.
- 现有的多属性组决策模型可能会导致粗的计算信息丢失.
- 在犹不决的模糊语言环境中,信息融合对三向决策模型构成挑战.
研究的目的:
- 为犹不决的模糊语言环境量身定制的新三向决策模式提出建议.
- 解决当前模型在处理犹偏好和信息融合方面的局限性.
- 为了实现更精细的计算,并减少信息丢失在复杂的决策场景.
主要方法:
- 开发了一种新的三方决策模型,整合了犹不决的模糊语言术语集.
- 采用D-S证据理论,使决策者对属性评估的信心融为一体.
- 在犹不决的模糊语言框架内,内置成本函数和计算决策规则值.
主要成果:
- 拟议的模型实现了决策者评估信息的细粒度融合.
- 它有效地考虑了与不同决策行动相关的成本函数.
- 通过数值示例和比较实验的验证证实了该模型的有效性.
结论:
- 新模型提供了一种有效的决策方法,使用犹不决的模糊语言信息.
- 与现有方法相比,它提供了更好的信息融合和计算细节性.
- 该模型适用于处理犹不决的模糊语言数据的智能分类和推系统.
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