Chapter 5 gives a summary of this paper and shows the prospects in the further study of fuzzy entropy based on credibility distributions.
第五章是对本文内容的总结及基于可信性分布的模糊熵问题的研究展望。
This paper has also given two methods to compute the local image dispersion, the image local entropy based method and the LBP based method.
同时本文给出了两种局部图像灰度离散度的计算方法：基于局部熵的计算方法以及基于LBP的计算方法。
The image threshold segmentation algorithm based on the Particle Swarm Optimization (PSO) combined with the rough entropy based on boundary region is presented.
提出一种基于微粒群优化(PSO)的边界区域粗糙熵的阈值图像分割算法。
To overcome the defects of image segmentation by maximum entropy, a new method of image segmentation was presented by using fuzzy maximum exponential entropy based on histogram.
针对最大熵阈值分割算法的计算缺陷，提出了一种基于直方图的模糊最大指数熵阈值图像分割新算法。
Moreover, the modified rough entropy and the fuzzy entropy based on equivalence relation is extended to the generalized modified rough entropy and the generalized fuzzy entropy.
并分别将基于不可分辨关系下的修正粗糙熵和模糊熵拓展到基于一般二元关系下的广义修正粗糙熵和广义模糊熵。
The L F signals are transformed by entropy based on wavelet packet algorithms for best basis selection, and the results are displayed in time- frequency space namely phase plane.
该方法采用基于熵的小波包最佳基选取准则，对局部损伤信号进行自适应小波包分解，将分解结果显示于时—频空间即相平面上。
Four kinds of condition entropy are defined in this paper. Accordingly, four kinds of entropy based methods for the attribute reduction in the rough set data analysis are proposed.
本文定义了四种条件熵，并在此基础上提出了四种基于熵的方法，以用于粗糙集数据分析中的属性简约。
The extracting method of process information entropy based on process steady entropy, double channel singular spectrum entropy and double channel power spectrum entropy was introduced.
对管线泄漏特征进行了分析，并介绍了基于过程平稳熵、双通道奇异谱熵和双通道能谱熵的过程信息熵特征提取方法。
Based on the study and research of the basic knowledge, this article focuses on the information entropy-based worm detection algorithm.
在前述基础知识的学习研究基础之上，本文重点对基于信息熵的蠕虫检测算法进行了一系列研究。
The experiments show that, compared with the entropy-based method, our method is simpler in the structure, and can improve the efficiency of classification.
经实验证明，用该方法构造的决策树与传统的基于信息熵方法构造的决策树相比较，复杂性低，且能有效提高分类效果。
This paper introduces a local entropy-based multi-resolution image fusion technique.
提出一种基于局部熵的多分辨图像融合算法。
This paper introduces a local entropy-based multi-resolution image fusion technique.
提出一种基于局部熵的多分辨图像融合算法。
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