1) fuzzy means clustering
模糊均值聚类
1.
Attribute means clustering is more robustthan fuzzy means clustering by theoretical analysis.
理论分析表明属性均值聚类是比模糊均值聚类更稳健的聚类方法,因此本文提出了基于属性均值聚类的入侵检测新方法。
2.
Attribute means clustering is more robust than fuzzy means clustering by theoretical analysis and numerical exampl e.
通过理论分析 ,属性均值聚类是比模糊均值聚类更稳健的聚类方法 。
3.
For the defect that pattern layer neurons are proportional to the number of the training samples, the method of fuzzy means clustering based on similarity index to decrease samples is proposed.
针对广义回归网络的模式层单元数目与样本数量成正比的问题,提出了基于相似度衡量的模糊均值聚类的样本精简方法。
2) fuzzy c-means clustering
模糊C均值聚类
1.
The analog circuits fault diagnosis based on fuzzy C-means clustering;
基于模糊C均值聚类的模拟电路故障诊断
2.
Segmentation of fat and lean meat in beef images based on fuzzy C-means clustering;
基于模糊C均值聚类的牛肉图像中脂肪和肌肉区域分割技术
3.
Brain MR image segmentation based on anisotropic Gibbs random field and fuzzy C-means clustering model;
基于Gibbs场与模糊C均值聚类的脑MR图像分割
3) fuzzy C-means clustering
模糊C-均值聚类
1.
Escaped toll analysis of ETC system customer data based on fuzzy C-means clustering;
基于模糊C-均值聚类的ETC系统客户的逃费分析研究
2.
In order to recognize the pollutant sources and build the correspondence relationships between contaminated sources and important pollutants,a set of intelligent recognizing method based on correspondence factor analysis and fuzzy C-means clustering(short for IRM-CFA&FCM) is developed.
为识别东湖污染物来源,建立排污口与主要致污因子之间的对应关系,提出了污染物来源智能识别方法;该方法巧妙耦合了对应分析、模糊C-均值聚类及聚类有效性函数等方法,并用加速遗传算法有效解决了这一复杂问题。
3.
To improve the accuracy of text clustering,fuzzy c-means clustering based on topic concept sub-space(TCS2FCM) is introduced for classifying texts.
为了改善文本聚类的准确度,提出用基于主题概念子空间的模糊c-均值聚类(TCS2FCM)方法来分类文本。
4) FCM
模糊C均值聚类
1.
A novel algorithm for discretization of continuous attributes in rough set theory based on FCM;
基于模糊C均值聚类的粗集理论连续属性的离散化新算法
2.
Based on the idea of combining models to improve prediction accuracy and robustness, the soft sensor model of the dry point of the first top naphtha is built by using FCM to divide a whole training dataset into several clusters with different centers.
应用多神经网络建立初顶石脑油干点软测量模型,首先采用模糊C均值聚类法将样本集分成具有不同聚类中心的子集,每个子集运用BP神经网络训练得出子模型,然后根据聚类后产生的隶属度将各子模型的输出加权求和获得初顶石脑油干点软测量值。
3.
With only pixel value information taken into account and non-robust Euclidean distance used as the distance measure standard, the classical Fuzzy C-means Clustering (FCM) algorithm lacks enough robustness in the image segmentation.
传统模糊C均值聚类(FCM)算法进行图像分割时仅利用了像素的灰度信息,并且使用对噪声较敏感的欧氏距离作为像素与聚类中心距离度量的标准,因此抗噪性能较差。
5) fuzzy C-means
模糊C均值聚类
1.
A new strategy for speaker recognition,triple-particle fuzzy C-means clustering(FCM),called TP-FCM,was proposed.
基于粒子群优化(particle swarm optim ization,PSO)提出一种说话人识别算法—三粒子模糊C均值聚类算法。
2.
The new algorithm is based on the principle of fuzzy C-means(FCM) clustering algorithm and the transiently chaotic neural network(TCNN) algorithm.
带容量约束的多车调度问题是典型的NP-hard问题,利用模糊C均值聚类算法的相似性分类原理及混沌神经网络的全局搜索能力和高搜索效率,提出了一种快速且易于实现的新的混合启发式算法。
3.
An algorithm on data association for multisensor multitarget tracking (MSMTT) based on fuzzy comparability and fuzzy C-means (FCM)is proposed.
利用模糊相似性和模糊C均值聚类算法相结合,提出一种求解多传感器多目标跟踪数据关联问题的方法。
6) fuzzy C-means cluster
模糊C-均值聚类
1.
Therefore,based on the fuzzy C-means cluster method and the compact and separate cluster validity function,a dynamic fuzzy C-means clustering method and corresponding cluster validity function based time are put forward,and a s.
鉴于目前常规聚类方法不同时具备这些能力,在模糊C-均值聚类和紧密与分离聚类有效函数的基础上,提出了能够处理高维时序聚类问题的动态模糊C-均值聚类分析方法和相应的时序聚类有效性函数,耦合二者建立了适用于汛期分期的有效模糊聚类分析方法,提出采用实码加速遗传算法优化求解,克服了模糊C-均值聚类方法常规迭代优化求解对初值敏感的困难,并给出了完备的建模步骤和模型的合理性检验。
2.
Using a modified Xie-Beni cluster validity index, the fuzzy c-means cluster algorithm with entropy regularization is extended to integrate fuzzy cluster and density estimation to identify TF components and derive TF energy mixture model that indicates the number of components in the s.
用修正的Xie-Beni聚类有效性指标对熵调整模糊c-均值聚类算法进行拓展将模糊聚类与密度估计相结合,实现了信号时频分量的识别和建模;信号的时频能量混合模型给出了信号分量的数目及其在时频面上所占据的区域。
3.
The paper based on previous studies, summing up the basic principles of Region Growing, Fuzzy C-means Cluster and BP(Back Propagation, BP) Network, using the function set and toolbox of MATLAB software, programming to separate cloud and fog on MTSAT-1R satellite image, recognizing and extracting fog areas.
本文在前人研究的基础上,总结了区域生长法、模糊C-均值聚类法和BP神经网络法的基本原理,并利用MATLAB软件强大的函数集和工具箱,编程实现了对MTSAT-1R卫星影像中云雾的分离处理,并对影像中的雾区进行识别、提取,研究成果为大雾遥感监测提供了依据。
补充资料：动态模糊聚类法
分子式：
CAS号：
性质：又称动态模糊聚类法。选定一批聚类中心，其指标能反映该类的特征，将样本向最近的聚类中心聚类。再根据分类结果确定新的聚类中心，其各项指标为该类中所有样本的相应指标的平均值。然后计算前后两聚类中心的差异，如差异大于某一阈值，说明分类不合理，需修改分类，即以新的聚类中心代替旧的聚类中心，直到前后两聚类中心的差异小于某一阈值，认为分类合理，从而终止分类过程。
CAS号：
性质：又称动态模糊聚类法。选定一批聚类中心，其指标能反映该类的特征，将样本向最近的聚类中心聚类。再根据分类结果确定新的聚类中心，其各项指标为该类中所有样本的相应指标的平均值。然后计算前后两聚类中心的差异，如差异大于某一阈值，说明分类不合理，需修改分类，即以新的聚类中心代替旧的聚类中心，直到前后两聚类中心的差异小于某一阈值，认为分类合理，从而终止分类过程。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条