融合模糊信息量与可信度，提升不确定性下的决策评估精度
A novel framework for MCDM based on Z numbers and soft likelihood function
- 基于模糊隶属度信息量与可信度构建软似然函数框架
- 在不确定环境下更准确提取专家评估中的有效信息
- 适合处理模糊、冲突的多源评估数据，如决策支持系统
在不确定环境下的信息管理流程优化已受到广泛关注。然而，如何从专家评估中获取准确且合理的评价仍是一个开放问题。直觉模糊集为处理不确定信息提供了有效途径。近期，Yager提出一种融合概率证据的新方法——软似然函数，用于处理不确定且冲突的信息。本文提出一种基于模糊隶属度信息量与可信度测度的新型软似然函数框架，旨在从不确定性中提取真正有用和有价值的信息。通过实例验证了该框架的有效性与正确性。此外，与现有方法的对比进一步证明了该框架的优越性。
原文摘要 · Abstract (English)
The optimization on the structure of process of information management under uncertain environment has attracted lots of attention from researchers around the world. Nevertheless, how to obtain accurate and rational evaluation from assessments produced by experts is still an open problem. Specially, intuitionistic fuzzy set provides an effective solution in handling indeterminate information. And Yager proposes a novel method for fusion of probabilistic evidence to handle uncertain and conflicting information lately which is called soft likelihood function. This paper devises a novel framework of soft likelihood function based on information volume of fuzzy membership and credibility measure for extracting truly useful and valuable information from uncertainty. An application is provided to verify the validity and correctness of the proposed framework. Besides, the comparisons with other existing methods further demonstrate the superiority of the novel framework of soft likelihood function.
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