搜索资源列表
kcluster
- 一种基于软划分方法的聚类方法——模糊k均值法聚类分析。-A division of methods based on soft clustering method- fuzzy k-means cluster analysis.
RepeatFCM
- FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分-FCM algorithm is a clustering algorithm based on the division, which makes the idea is to be divided into clusters with the greatest simila
pb222
- 软测量氧含量原始数据分工况聚类函数及代码-Raw data measuring oxygen content in the soft state clustering functions and code division
rbf_gzlsd
- 软测量氧含量原始数据及matlab工况聚类代码-Measuring the oxygen content of soft raw data and clustering matlab code condition
rbf_jm
- 软测量氧含量数据工况聚类、建模matlab实现代码-Measuring the oxygen content of soft data clustering conditions, modeling matlab implementation code
sanlei
- 软测量氧含量数据工况聚类、建模matlab实现代码2-Measuring the oxygen content of soft data clustering conditions, modeling the implementation code 2 matlab
shuchufenlei
- 软测量氧含量数据工况聚类、建模matlab实现代码3-Measuring the oxygen content of soft data clustering conditions, modeling matlab implementation code 3
FCM
- 核聚类算法：聚类是将一组给定的未知类标号的样本分成内在的多个类别，使得同一类中 的样本具有较高的相似度，而不同类中的样本差别大。侧重于软聚类（模糊C-均值——FCM），但其描述手段同样适合于硬聚 类（HCM）等同类问题。-Clustering algorithm: cluster is a group of unknown samples given class label into internal multiple categories, so that the same class
paosang_v12
- 用MATLAB实现动态聚类或迭代自组织数据分析，ofdm系统仿真 含16qam调制 fft 加窗 加cp等模块，比较了软阈值，硬阈值及当今各种阈值计算方法。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, ofdm system simulation including 16qam modulation fft windowing modules plus cp, Comparison of
junyie
- 基于欧几里得距离的聚类分析，matlab开发工具箱中的支持向量机，比较了软阈值，硬阈值及当今各种阈值计算方法。- Clustering analysis based on Euclidean distance, matlab development toolbox support vector machine, Comparison of soft threshold and hard threshold and today various threshold calculation metho
faotai_V3.6
- 比较了软阈值，硬阈值及当今各种阈值计算方法,可实现对二维数据的聚类，有PMUSIC 校正前和校正后的比较。- Comparison of soft threshold and hard threshold and today various threshold calculation method, Can realize the two-dimensional data clustering, A relatively before correction and after correctio