信息图表
联合熵
联合量子熵
数学
信息论
熵(时间箭头)
最大熵热力学
最大熵概率分布
雷诺熵
广义相对熵
统计物理学
最大熵原理
统计
物理
量子力学
量子
量子不和谐
量子纠缠
作者
Tianxiang Zhan,Jiefeng Zhou,Zhen Li,Yong Deng
标识
DOI:10.1016/j.chaos.2024.114976
摘要
The concept of entropy has played a significant role in thermodynamics and information theory, and is also a current research hotspot. Information entropy, as a measure of information, has many different forms, such as Shannon entropy and Deng entropy, but there is no unified interpretation of information from a measurement perspective. To address this issue, this article proposes Generalized Information Entropy (GIE) that unifies entropies based on mass function. Meanwhile, GIE establishes the relationship between entropy, fractal dimension, and number of events. Therefore, Generalized Information Dimension (GID) has been proposed, which extends the definition of information dimension from probability to mass fusion. GIE plays a role in approximation calculation and coding systems. In the application of coding, information from the perspective of GIE exhibits a certain degree of particle nature that the same event can have different representational states, similar to the number of microscopic states in Boltzmann entropy.
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