Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: General framework and practical models

被引：32

作者：

Denoeux, Thierry ^{[1
,2
,3
]}

机构：

[1] Univ Technol Compiegne, CNRS, UMR Heudiasyc 7253, Compiegne, France

[2] Inst Univ France, Paris, France

[3] Univ Technol Compiegne, Rue Roger Couttolenc,CS 60319, F-60203 Compiegne, France

来源：

FUZZY SETS AND SYSTEMS | 2023年 / 453卷

关键词：

Belief functions; Evidence theory; Possibility theory; Random sets; Uncertainty; BELIEF FUNCTION JUSTIFICATION; EXTENSIONS;

D O I：

10.1016/j.fss.2022.06.004

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.(c) 2022 Elsevier B.V. All rights reserved.

引用

页码：1 / 36

页数：36

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