Description: 基于信息熵的快速模糊C均值聚类算法。提出了一中新的快速算法，效果很好。-Based on the information entropy fuzzy c-means algorithm quickly clustering algorithm. Put forward a new fast algorithm, the effect is very good.
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Author:白鑫 |
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Description: 2012年全国大学生数学建模竞赛题目--葡萄酒的评价。文件中包含葡萄酒成分数据（Excel文件）、各种数据统计处理的MATLAB函数（已封装好，直接调用），包括：数据预处理、灰色预测、灰色关联度、基于熵权法的模糊综合评价、主成分分析、系统聚类、多元线性回归等程序。-Wines Evaluation from the title of 2012 National Undergraduate Mathematical ModelingContest. The file includes many MATLAB codes about statistical analysis of data.For example: Principal component analysis Grey Relational Analysis Fuzzy Comprehensive Evaluation Based on Entropy Method System Clustering Grey Prediction Multiple linear regression and so on. We can call them directly. Platform: |
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Author:charidong |
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Description: 在众多聚类算法中,基于划分的模糊聚类算法是模式识别中最常用的算法类型之一.至今,文献中仍不断
有关于基于划分的模糊聚类算法的研究成果出现.为了能更为系统和深入地了解这些聚类算法及其性质,本文从改
变度量方式、改变约束条件、在目标函数中引入熵以及考虑对聚类中心进行约束等几个方面,对在 C-均值算法的
基础上得到的基于划分的模糊聚类算法作了综述和评价,对各典型算法的优缺点进行了实验比较分析.指出标准
FCM算法被广泛应用的原因之一是它对数据的比例变化具有鲁棒性,而其他类似的算法对这种比例变化却很敏感,
并以极大熵方法为例进行了比较实验.最后总结了基于划分的模糊聚类算法普遍存在的问题及其发展前景. -Fuzzy partitional clustering algorithms are widely used in pattern recognition field. Until now, more
and more research results on them have been developed in the literature. In order to study these algorithms
systematically and deeply, they are reviewed in this paper based on c-means algorithm, from metrics, entropy, and
constraints on membership function or cluster centers. Moreover, the advantages and disadvantages of the typical
fuzzy partitional algorithms are discussed. It is pointed out that the standard FCM algorithm is robust to the scaling
transformation of dataset, while others are sensitive to such transformation. Such conclusion is experimentally
verified when implementing the standard FCM and the maximum entropy clustering algorithm. Finally, the
problems existing in these algorithms and the prospects of the fuzzy partitional algorithms are discussed. Platform: |
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Author:成方 |
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Description: 为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识
别。对同一负荷下的已知故障信号进行变分模态分解，利用
奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法
应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。- In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value
decomposition was proposed for fault diagnosis using standard
fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were
decomposed by VMD, and the modes characteristics were
further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the
test samples were clustered by a Hamming nearness approach,
and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under
variable loads. By comparing with a method based on EMD,
this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo Platform: |
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Author:陈帅 |
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Search - fuzzy entropy clustering