Complex evidence theory, as a generalized D-S evidence theory, has attracted academic attention because it can well express uncertainty by means of complex basic belief assignment (CBBA), and realize uncertainty reasoning by complex combination rule. However, the uncertainty measurement in complex evidence theory is still an open issue. In order to make better decisions, a complex pignistic belief transformation (CPBT) method has been proposed to assign CBBAs of multi-element focal elements to subsets. The essence of CPBT is the redistribution of complex mass function by means of the concept of fractal. In this paper, based on fractal theory, experimental simulation and analysis have been carried out on the generation process of CPBT in time dimension. Then, a new fractal-based complex belief (FCB) entropy is proposed to measure the uncertainty of CBBA. Finally, the properties of FCB entropy are analyzed, and several examples are used to verify its effectiveness.

翻译：复杂证据理论作为D-S证据理论的推广，因其通过复基本信任分配（CBBA）有效表达不确定性，并借助复组合规则实现不确定性推理而受到学术关注。然而，复杂证据理论中的不确定性度量仍是一个待解决的问题。为优化决策，已有研究提出复pignistic信任变换（CPBT）方法，将多元素焦元的CBBA分配给子集。CPBT的本质是通过分形概念对复质量函数进行再分配。本文基于分形理论，从时间维度对CPBT的生成过程开展实验仿真与分析，进而提出一种基于分形的新型复信任（FCB）熵用于度量CBBA的不确定性。最后，本文分析了FCB熵的性质，并通过算例验证其有效性。