对于正常云相似性的语言多属性决策方法的研究
Changlin Xu1,2, Li Yang1
1School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.
Heliyon
|October 27, 2023
概括
本研究引入了一种新的正常云相似性方法,以解决语言多属性决策中的不确定性. 与现有技术相比,该方法显示出优越的相似性歧视,分类准确性和效率.
科学领域:
- 决策科学 决策科学
- 人工智能的人工智能
- 可能性理论概率理论.
背景情况:
- 语言多属性决策 (LMAD) 面临着不确定性和语言转换的挑战.
- 现有的方法难以有效量化和比较不确定的语言信息.
研究的目的:
- 提出一种基于正常云相似性的新决策方法,以处理语言不确定性.
- 开发一种新的相似度测量标准云使用瓦瑟斯坦距离.
- 在语言决策场景中验证方法的性能.
主要方法:
- 开发了一种使用瓦瑟斯坦距离和正常云特征曲线的正常云相似度量.
- 分析了拟议的相似度指标的属性.
- 使用数值模拟和时间序列分类实验进行性能评估.
- 将该方法应用于语言多属性决策,使用TOPSIS (通过与理想解决方案相似的优先顺序技术) 方法.
主要成果:
- 拟议的正常云相似性方法对不确定的语言信息表现出强大的区分能力.
- 在时间序列数据分类实验中实现了高分类准确性.
- 证明了较低的计算成本 (CPU时间).
- 成功应用于用于计划排名的语言多属性决策.
结论:
- 新的正常云相似度有效地解决了LMAD中的不确定性语言转换问题.
- 该方法为涉及语言数据的复杂决策问题提供了强大而高效的替代方案.
- 与TOPSIS的整合为最终方案选择提供了实际框架.
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