论文标题
指数否定概率分布
Exponential Negation of a Probability Distribution
论文作者
论文摘要
否定操作在智能信息处理中很重要。与现有的算术否定不同，本文提出了指数否定。新的否定可以看作是一种几何否定。研究了所提出的否定的一些基本特性，我们发现固定点是统一的概率分布。否定是熵的增加操作，所有概率分布将在多次否定迭代后收敛到均匀分布。收敛的迭代次数与分布中的元素数量成反比。一些数值示例用于说明拟议的否定的效率。
Negation operation is important in intelligent information processing. Different with existing arithmetic negation, an exponential negation is presented in this paper. The new negation can be seen as a kind of geometry negation. Some basic properties of the proposed negation is investigated, we find that the fix point is the uniform probability distribution. The negation is an entropy increase operation and all the probability distributions will converge to the uniform distribution after multiple negation iterations. The number of iterations of convergence is inversely proportional to the number of elements in the distribution. Some numerical examples are used to illustrate the efficiency of the proposed negation.
