论文标题
使用认知随机模糊集的模糊和不确定证据的推理：一般框架和实用模型
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models
论文作者
论文摘要
我们介绍了一种用模糊或清晰的证据推理的认知随机模糊集的一般理论。该框架概括了dempster-shafer的信仰功能理论和可能性理论。独立的认知随机模糊集由广义的产品交流规则组合，该规则扩大了Dempster的结合信念函数的规则，也扩展了可能性分布的产品结合组合。我们引入高斯随机模糊数及其多维扩展，高斯随机模糊向量，作为量化标量或矢量数量不确定性的实用模型。得出了高斯随机模糊数和向量的组合，投影和空置扩展的封闭形式表达式。
We introduce a general theory of epistemic random fuzzy sets for reasoning with fuzzy or crisp evidence. This framework generalizes both the Dempster-Shafer theory of belief functions, and possibility theory. Independent epistemic random fuzzy sets are combined by the generalized product-intersection rule, which extends both Dempster's rule for combining belief functions, and the product conjunctive combination of possibility distributions. We introduce Gaussian random fuzzy numbers and their multi-dimensional extensions, Gaussian random fuzzy vectors, as practical models for quantifying uncertainty about scalar or vector quantities. Closed-form expressions for the combination, projection and vacuous extension of Gaussian random fuzzy numbers and vectors are derived.
