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一种基于犹不决的模糊语言互动程度的二阶增量模糊测量识别方法,及其在信用评估中的应用
Mu Zhang1, Wen-Jun Li2,3, Cheng Cao2,3
1School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, 550025, China. zhangmu01@163.com.
Scientific reports
|April 14, 2024
概括
这项研究引入了一种新的方法,用于识别二级附加模糊措施,使用犹不决的模糊语言术语集来捕捉属性交互性. 该方法通过在评估中纳入模糊性和犹来增强多属性决策 (MADM).
科学领域:
- 模糊的数学 模糊的数学
- 决策科学 决策科学
- 游戏理论 游戏理论
背景情况:
- 传统的模糊度量很难代表特征交互性的专家评估中固有的模糊性和犹.
- 超模块化游戏理论为定义属性交互性提供了一个框架,但语言表征仍然是一个挑战.
研究的目的:
- 开发一种新的方法来识别二级附加模糊指标,有效地将模糊和犹纳入属性交互性.
- 通过提供对属性相互作用的更细致的评估来增强多属性决策 (MADM) 过程.
主要方法:
- 使用犹不决的模糊语言术语集 (HFLTS) 来描述属性交互性,将由无上下文语法生成的语言表达式转换为HFLTS.
- 使用犹不决的模糊语言权重功率平均 (HFLWPA) 运算符来汇总专家评估,并使用欧几里德距离定义犹不决的模糊语言交互度 (HFLID).
- 提出了基于HFLID的2级附加模糊测量识别方法,并为MADM应用了Choquet模糊积分.
主要成果:
- 成功开发和验证了一种新的方法来识别二级附加模糊指标,以解释模糊和犹.
- 通过对中国大数据上市公司的信用评估应用来证明该方法的有效性和可行性.
- 拟议的方法准确地反映了属性交互性,丰富了二级附加模糊测量的理论框架.
结论:
- 开发的基于HFLTS的方法为评估模糊指标中的属性交互性提供了一个强大的框架.
- 这项研究扩大了二级附加模糊指标在复杂的多属性决策场景中的适用性.
- 该研究成功地将语言的不确定性和专家的犹融入了模糊的测量识别和MADM中.
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