A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm

Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.

Improved evidential fuzzy c-means method

Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.

基于FCM算法的中小型转动设备故障检测研究

针对现有算法在中小型转动设备故障检测中存在的收敛速度慢、故障识别率低等问题,提出一种基于FCM融合算法的故障检测方案研究。对原始故障集做降噪处理,基于模糊熵值理论在多尺度条件下提取故障向量的隶属度;利用GA算法优化FCM算法的迭代性能和收敛性能,分别更新故障特征向量模糊隶属度矩阵和聚类中心矩阵,以达到改善聚类精度,提高故障识别率的目的。实验结果显示,该算法在不同的聚类中心数量及故障类别的条件下,能够获得更好的聚类效果和更高的收敛速度,训练集合和测试集的平均故障识别分别可以达到99.19%和98.23%。

Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

融合改进FCM与PFS的知识供需匹配

为提升知识资源的有效配置,缓解用户“知识迷向”问题,设计一套知识供需匹配方法。依据DBSCAN算法确定FCM算法的聚类数目,增强聚类效果;基于改进FCM进行区域划分实现匹配空间压缩,提升算法效率。在此基础上,构建模糊关联匹配度模型,通过融合Zadeh与PFS模糊算子改进相似度计算,兼顾用户需求与既有知识间的相关度,确定匹配结果。实验分析表明,其在知识匹配有效性方面具有一定的比较优势。

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

基于FCM算法的多属性分析技术在河道砂体精细刻画中的应用——以西湖凹陷T气田为例

西湖凹陷T气田经过十多年的勘探与开发,亟需在主力层花港组内寻找潜力目标。该区为浅水三角洲沉积体系,岩性组合在空间上变化快,为了精确识别河道砂体及其边界,在海上少井条件下利用三维地震资料识别并刻画河道砂体。在等时地层划分的基础上,对目的层段进行岩石物理性质分析,通过地震沉积学的技术方法结合岩芯及测井等资料,对沉积微相做出初步判断,在此基础上提取6类48种地震属性,与砂厚及各属性之间进行相关性分析,对地震属性进行优选,将优选出的3种反映地质体边界、岩性较好的地震属性采用基于模糊C-均值(FCM)算法的多属性聚类分析,以达到数据降维、减少冗余的效果,研究分流河道沉积体系的整体展布规律。再进行多属性RGB融合显示,增强河道砂体边界的刻画,结合构造特征以及预测的砂体厚度综合分析,提出有利目标区,为后续油田滚动开发及井位部署提供依据。

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

基于多特征和FCM的图像边缘检测方法

提出了一种新的基于多特征和FCM的边缘检测算法.该方法根据边缘点附近灰度分布特点构造了多个反映边缘特性的特征分量,并利用输入图像提取该组特征分量,组成一个反映图像边缘特征的数据集.用FCM聚类算法将该数据集分为两类,即边缘点数据和非边缘点数据,实现边缘检测.该方法无需确定阈值,对弱边缘检测较敏感,在特征的选取上充分考虑了边缘和噪声的本质区别,因而具有优异的抗噪性能.

模糊C-均值(FCM)聚类算法的实现

传统的FCM算法能够将靠近边界的具有固有形状的两个簇合并成为一个大的簇。然而,对于一些稍微复杂的数据,如果没有其它的像去除小簇之类的机制的话,FCM算法很难将非常接近的类聚类到一起。给出的聚类算法是在传统FCM算法的循环之后添加了去除掉空簇的步骤,解决了上述很难将非常接近的类聚到一个簇中的问题。另外,为便于选出最优结果,在递归之后又添加了计算聚类有效性的步骤。最后用Java实现了该算法并在数据集上进行了实验,证实了改进方法的有效性。

一种基于三角模糊数多指标信息的FCM聚类算法

针对一类具有不确定性三角模糊数多指标信息的聚类分析问题,基于传统的数值信息FCM聚类算法,提出一种新的聚类分析算法.首先描述了具有三角模糊数多指标信息的聚类分析问题,提出并证明了基于三角模糊数多指标信息的关于最优划分和最优聚类中心确定的两个定理;然后根据这两个定理,进一步给出了基于三角模糊数信息的FCM聚类算法的迭代步骤;最后通过一个算例说明了该聚类算法的具体应用.

半监督FCM聚类算法目标函数研究

分析了现有半监督FCM算法目标函数的物理意义和平衡系数α的选取,说明Stutz对Pedrycz目标函数的修改使半监督的物理意义更清楚,它在α=1,0时均退化为标准FCM算法,给出了修改后SS-FCM算法的交替求解过程。实验结果:(1)修改算法与Pedrycz算法有相同的半监督作用和清楚的物理解释;(2)对labeled样本采用FCM算法赋值比用随机数的收敛稳定性高;(3)优选的少量labeled样本,使用模糊协方差的SS-CFCM算法提高了聚类准确性和收敛速度。

FCM算法的改进及仿真实验研究

针对FCM原型算法的不足,提出一种新的改进方法,并进行仿真实验研究。利用主成分分析方法对原始数据集的指标进行筛选,应用Relief算法对入选指标计算权重。在此基础上,对FCM算法进行了改进。应用模糊划分系数F(CR)和平均模糊熵H(CR)这两个指标对算法的性能进行了评价。仿真实验结果表明,改进后的FCM算法对样本集数据的分类符合率达到了91.5%,其模糊划分系数F(CR)和平均模糊熵HC(R)分别为0.924和-0.062。改进后的FCM算法分类性能优于FCM原型算法,在应用中可以取得更好的效果。

基于SFLA-FCM聚类的城市交通状态判别研究

针对城市道路交通状态判别的问题,提出了一种混合蛙跳算法(SFLA)与模糊C-均值算法(FCM)相结合的SFLA-FCM聚类算法。SFLA是一种全新的后启发式群体进化算法,具有高效的计算性能和优良的全局搜索能力。SFLA-FCM使用SFLA的优化过程代替FCM的基于梯度下降的迭代过程,有效地避免了FCM对初值敏感及容易陷入局部极小的缺陷。将该算法用于城市交通流数据的聚类分析结果表明,与单一FCM聚类算法相比,SFLA-FCM聚类算法更准确,效果更佳,能够快速而有效地对城市交通流状况进行判别,为动态交通拥堵预警和交通诱导策略的制定提供依据。

基于随机森林模型的长江流域分区多源融合降水模拟方法研究

基于3种卫星降水产品,提出了一种基于随机森林模型的长江流域分区多源融合降水模拟算法(FCM-RF算法)。采用模糊C均值算法,结合地面观测站点资料对长江流域进行降水区域划分,引入降水比降刻画降水空间性,进一步通过普通克里金插值法优化融合结果,得到一套长江流域空间分辨率为0.25°×0.25°的多源融合降水产品,并对其进行了评估。结果表明:FCM-RF算法在长江流域具有良好的表现,可以有效提高原始卫星降水产品对于降水事件的捕捉能力,在验证站点模拟降水量与实测降水量的相关系数可达到0.76;FCM-RF算法在年际上具有相似变化特征,对于春秋季降水的敏感性较高,在夏季由于强降水影响表现欠佳,冬季由于雨量稀少、存在固态降水,呈现出误差小、相关系数较低的特点;FCM-RF算法在东南地区具有较强的降水捕捉能力,在青藏高原地区的准确性较低。

FCM算法用于灰度图像分割的初始化方法的研究

模糊C均值聚类(FCM)算法是一种经典的模糊聚类分析方法,但其算法初始聚类中心集是随机选取的,从而造成算法的性能强烈的依赖聚类中心集的初始化。提出了一种改进的基于多项式求解的FCM(PFCM)算法,该算法基于求解多项式的根来确定数据集初始聚类中心集,很好地解决了数据初始聚类中心集问题,使数据初始聚类中心集代表了数据集类别的特征,在此基础上,采用FCM算法得到聚类中心集的近似最优解。