论文标题
基于分形的信仰熵
Fractal-based Belief Entropy
论文作者
论文摘要
Dempster-Shafer证据理论（DSET）中基本概率分配（BPA）的总不确定性测量一直是一个空旷的问题。尽管一些学者提出了BPA的各种测量和熵，但由于存在不和谐和非特异性，但没有方法可以合理地测量BPA。为了利用BPA进行实际决策，BPA的有效概率转化是一种重要方法。在本文中，我们基于分形概念模拟了高概率转换（PPT）过程，该过程详细描述了PPT过程，并显示了信息量变化过程中的信息量变化的过程。基于转换过程，我们提出了一种新的信念熵，称为基于分形信念（FB）熵。验证后，就总不确定性测量和物理模型一致性而言，FB熵优于所有现有的信念熵。
The total uncertainty measurement of basic probability assignment (BPA) in Dempster-Shafer evidence theory (DSET) has always been an open issue. Although some scholars put forward various measurements and entropies of BPA, due to the existence of discord and non-specificity, there is no method can measure BPA reasonably. In order to utilize BPA to practical decision-making, pignistic probability transformation of BPA is a significant method. In the paper, we simulate the pignistic probability transformation (PPT) process based on the fractal idea, which describes PPT process in detail and shows the process of information volume changes during transformation intuitively. Based on transformation process, we propose a new belief entropy called fractal-based belief (FB) entropy. After verification, FB entropy is superior to all existing belief entropies in terms of total uncertainty measurement and physical model consistency.
