提出新方法融合不同来源证据，提升复杂场景下的信息整合能力。
Evidential Information Fusion on Possibilistic Structure
- 将信念函数转化为可能性结构，用网络表征子集间关系
- 引入三角范数族实现自适应融合，支持非独立信源与冲突处理
- 适用于异构信息融合，适合需要灵活组合的决策系统
Dempster规则是结合多个可靠来源信念函数的基础工具，但其基于交集的语义施加了强结构限制，难以应对复杂源状态和多样化融合场景。为此，我们基于等可能性原理，提出一种可逆变换，将信念函数映射到幂集上的可能性结构。该变换通过信念演化网络显式刻画子集间的关联关系，提供了超越传统质量函数结构的更灵活证据表示。在此基础上，引入三角范数族构建通用且自适应的证据信息融合框架。与基于Dempster语义的方法不同，该框架支持更灵活的组合行为，在非独立信源融合、冲突管理、参数化组合设计及异构信息融合方面具有优势。
原文摘要 · Abstract (English)
Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in handling complex source states and diverse information fusion scenarios. To overcome this limitation, we propose a reversible transformation, derived from the isopignistic principle, between belief functions and a possibilistic structure defined on the power set. In this transformation, the relationships among subsets are explicitly characterized by a belief evolution network, which provides a more flexible representation of evidential information beyond the conventional mass function structure. On this basis, we further introduce the triangular norm family to develop a general and adaptive evidential information fusion framework. Unlike fusion methods rooted in Dempster semantics, the proposed framework supports more flexible combination behaviors and exhibits advantages in non-distinct source fusion, conflict management, parametric combination design, and heterogeneous information fusion.
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