论文标题
质量功能的高阶信息量
Higher order information volume of mass function
论文作者
论文摘要
在一定时刻，可以通过香农熵准确测量在概率空间中表示的信息量。但是在现实生活中，事物的结果通常会随着时间的流逝而发生变化，未来包含的信息量的预测仍然是一个悬而未决的问题。 Deng近年来提出的Deng熵广泛用于衡量不确定性，但其物理解释是有争议的。在本文中，我们为邓熵提供了基于分形思想的新解释，并提出了其概括，称为时间分形（TFB）熵。 TFB熵被认为是通过分配时间来预测一段时间内的不确定性，其最大值（称为高阶信息质量函数（HOIVMF））可以表达比所有现有方法更不确定的信息。
For a certain moment, the information volume represented in a probability space can be accurately measured by Shannon entropy. But in real life, the results of things usually change over time, and the prediction of the information volume contained in the future is still an open question. Deng entropy proposed by Deng in recent years is widely applied on measuring the uncertainty, but its physical explanation is controversial. In this paper, we give Deng entropy a new explanation based on the fractal idea, and proposed its generalization called time fractal-based (TFB) entropy. The TFB entropy is recognized as predicting the uncertainty over a period of time by splitting times, and its maximum value, called higher order information volume of mass function (HOIVMF), can express more uncertain information than all of existing methods.
