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Probability Distribution-Based Processing Model of Probabilistic Linguistic Term Set and Its Application in Automatic Environment Evaluation
International Journal of Fuzzy Systems ( IF 3.6 ) Pub Date : 2021-03-24 , DOI: 10.1007/s40815-021-01060-7
Yu Li , Xiao-kang Wang , Jian-qiang Wang , Jun-bo Li , Lin Li

The probabilistic linguistic term set (PLTS) is an effective tool for describing linguistic value in the process of decision-making. However, due to its complex structure, there remains numerous defects in utilizing the PLTS information. Therefore, by connecting the PLTS with discrete probability distribution, this study investigates a novel probability distribution-based processing model to process PLTS and further develops a practical multi-criteria decision-making (MCDM) approach for addressing automatic environmental monitoring evaluation problem. First, we propose two types of pre-processing methods to complete probability normalization. Accordingly, the normalized PLTS is represented in the form of probabilistic linguistic probability distribution (PLPD). Then, probabilistic linguistic earth-mover (PLEM) distance is constructed to measure the divergency between PLPDs, and a probabilistic linguistic probability distribution weighted mean (PLPDWM) is also developed to facilitate information fusion. On this basis, an aggregation operator-based MCDM approach is established that integrates the probability distribution-based PLTS processing methods and the proposed worst-priority weight (WPW) method. Finally, the developed approach is demonstrated by solving a practical atmospheric pollutant evaluation problem and its strengths are verified through further sensitivity analysis and comparative analysis.



中文翻译:

基于概率分布的概率语言术语集处理模型及其在环境自动评估中的应用

概率语言术语集(PLTS)是描述决策过程中语言价值的有效工具。然而,由于其复杂的结构,在利用PLTS信息方面仍然存在许多缺陷。因此,通过将PLTS与离散概率分布联系起来,本研究研究了一种基于概率分布的新型处理模型来处理PLTS,并进一步开发了一种实用的多准则决策方法(MCDM),以解决自动环境监测评估问题。首先,我们提出两种预处理方法来完成概率归一化。因此,归一化的PLTS以概率语言概率分布(PLPD)的形式表示。然后,构建概率语言地球移动器(PLEM)距离以测量PLPD之间的差异,并且还开发了概率语言概率分布加权平均数(PLPDWM)以促进信息融合。在此基础上,建立了一种基于聚集算子的MCDM方法,该方法将基于概率分布的PLTS处理方法与建议的最坏优先权重(WPW)方法集成在一起。最后,通过解决实际的大气污染物评估问题证明了该方法的可行性,并通过进一步的敏感性分析和比较分析验证了其优势。建立了基于聚集算子的MCDM方法,该方法将基于概率分布的PLTS处理方法与建议的最坏优先权重(WPW)方法集成在一起。最后,通过解决实际的大气污染物评估问题证明了该方法的可行性,并通过进一步的敏感性分析和比较分析验证了其优势。建立了基于聚集算子的MCDM方法,该方法将基于概率分布的PLTS处理方法与建议的最坏优先权重(WPW)方法集成在一起。最后,通过解决实际的大气污染物评估问题证明了该方法的可行性,并通过进一步的敏感性分析和比较分析验证了其优势。

更新日期:2021-03-25
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