Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.

Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

NEW SHADOWED C-MEANS CLUSTERING WITH FEATURE WEIGHTS

Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.

Research on Wind Power Prediction Modeling Based on Adaptive Feature Entropy Fuzzy Clustering

Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.

Improved method for the feature extraction of laser scanner using genetic clustering

Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.

基于属性权重的Fuzzy C Mean算法

提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFCM算法的聚类算法.CF-WFCM算法强化重要属性在聚类过程中的作用,消减冗余属性的作用,从而改善聚类的效果.我们选取了部分UCI数据库进行实验,实验结果证明:CF-WFCM算法的聚类结果优于FCM算法的聚类结果.函数CFuzziness(w)不仅可以评价属性的重要性,而且可以评价属性评价函数的优劣.实验说明了这一问题.最后我们对CF-WFCM算法进行了讨论.

基于WFCM算法在MRI图像分割中的应用

模糊C均值聚类(FCM)算法是一种基于非监督聚类算法。样本加权模糊C均值聚类(WFCM)算法是FCM算法的改进,该算法能够明显提高收敛速度和聚类的准确性。无论是FCM算法还是WFCM算法,对噪声都相对敏感,而且聚类数目仍然需要人工确定。在此提出一种改进算法,首先通过偏微分方程(PDE)降噪算法对原始脑MRI医学图像进行处理;其次利用聚类有效性确定最佳聚类数目,对WFCM算法进行改进;最后利用本文改进算法对图像进行聚类分割。实验表明,该方法是一种具有自动分类能力、抗噪性较好的模糊聚类图像分割算法。

基于空间加权距离的自适应Fuzzy C-Means算法研究

空间聚类不仅应考虑GIS对象属性特征的相似性,还应考虑对象的空间邻近性。不同属性、位置特征在聚类中起到的作用不同。采用信息熵方法计算空间距离中各属性距离、位置距离的权重,权值大小用于度量相应特征在fuzzy c-means隶属度计算时的作用大小,并引入相似性指标,当两个聚类之间的相似度高于某个合并阈值时,则对应的一对聚类进行合并,从而克服需预先设置聚类类数的问题。通过应用实例的聚类有效性分析,与普通空间距离相比,基于空间加权距离的FCM算法具有稳定性和有效性。

FCM测站定权的LAGEOS-2卫星精密定轨影响分析

由于卫星激光测距(satellite laser ranging, SLR)台站的测距精度和稳定性存在差异,精密定轨中需要对不同性能的台站赋予不同权重。本文将模糊C均值(fuzzy C-means, FCM)聚类定权应用于LAGEOS-2精密定轨,并比较原始测站定权和FCM定权对轨道精度的影响。结果表明:1)相较于原始测站定权,FCM定权更能反映各SLR台站的性能,提高定轨精度和观测值数量;2)对于ILRS四个质量分析机构,原始测站定权下JCET发布的质量报告在定轨精度上略优于其他中心,采用FCM定权后,以4个机构发布的质量报告进行定轨的结果在同一水平,轨道精度为4.83~4.86 cm。

基于迭代模糊聚类算法的多源异构电力数据集成

在电力数据采集过程中,极易出现传感器故障、通信中断、数据丢失等问题,使得数据出现质量差异。多源数据格式与语义的不一致增加了数据集成的复杂性,导致电力数据集成效率较低。为此,提出基于迭代模糊聚类算法的多源异构电力数据集成方法。构建数据集成框架,通过语言生成器统一数据格式,利用迭代模糊聚类算法,模糊划分数据。反复迭代处理聚类中心和任意一个电力数据之间的欧几里得距离,获取最优聚类距离,采用频繁项集,获取数据功率谱密度,构建集成目标,设计电力数据集成流程。通过实验结果可知,应用该方法仅存在50 bit的数据集成误差,且电力数据集成效率较高。

跟驰工况下考虑风险分布的驾驶风格分类

车辆跟驰工况下,为通过驾驶场景中各因素的风险分布研究驾驶员特性,实现车路交互下的驾驶风格分类,文章提出一种基于改进的模糊综合评价法的驾驶风格分类方法。通过驾驶模拟器采集试验数据,并将车辆行驶参数和安全势场作为分类的特征参数;使用组合权重法对模糊综合评价法的权重集进行改进,从而对各特征参数赋予相应的权重,再通过改进的模糊综合评价法将驾驶风格分为冷静型、普通型、激进型3类;最后通过K-means聚类算法验证上述方法的合理性。改进的模糊综合评价法分类结果与K-means聚类结果的对比表明,两者的差异率仅为2%,且当聚类簇数为3时,轮廓系数高达0.685,即与无监督学习算法相同。研究结果表明,使用该文模糊综合评价法可以实现对驾驶风格的有效分类。

地空协同场景下加权模糊聚类用户簇划分方法

为了解决应急通信场景下使用无人机作为空中基站进行辅助通信时涉及的无人机基站部署策略中的用户簇划分问题,在兼顾无人机基站性能和用户体验的条件下,提出一种基于特征加权的模糊聚类(Improved FCM)算法。首先,根据每个无人机基站的信号覆盖范围和最大服务用户数量的性能约束,针对随机分布条件下的用户簇在划分过程中算法计算量大不易收敛的问题,提出一种基于距离加权的特征加权节点数据投影算法;其次,针对同一用户处于多个簇有效范围内时用户划分的有效性和无人机基站资源的最大化利用问题,提出一种基于用户位置和无人机基站负载均衡的价值加权算法。实验结果表明,所提方法充分满足无人机基站的服务性能约束,且与几何分形法(GFA)、谱聚类(Sp-C)等算法相比,特征加权模糊聚类算法获得的平均负载率和覆盖比是最优的,分别达到了0.774和0.0263,因此,该算法可为应急通信场景下的用户簇划分问题提供一种可行的解决方案。

基于WFC和MI的主题句提取方法

提出一种基于加权模糊聚类(WFC)和互信息(MI)的主题句提取方法,使主题句尽可能全面覆盖全文主题的同时,缩减自身的冗余,以提高摘要效率,采用加权模糊聚类的方法对文本句子进行分类,对在同一类中的句子使用比较互信息的方法进行排名处理,从而获得高质量的摘要。实验结果表明,与传统聚类方法比较,该方法的正确率提高约15%,可以达到约70%的精确度,并在阅读摘要时能够基本正确地获取文本信息。

基于优化初始聚类中心的改进WFCM图像分割算法

针对模糊C均值聚类算法在分割图像时容易陷入局部最优的问题,提出一种改进的加权模糊C均值聚类图像分割算法。该算法借助图像直方图势函数,找出图像直方图上潜在的图像分割点;再基于Fisher判别思想构造关于模糊分割初始聚类中心的最优化问题,以期求取合理初始聚类中心,避免算法陷入局部最优。仿真结果表明,改进的加权模糊聚类图像分割算法能够有效避免陷入局部最优,具有更高的分割效率。

一种带有Fuzzy聚类方法的ABC分析

提出一种带有Fuzzy聚类方法的ABC分析,广泛适用于需要考虑对象的多方面因素,而且某些因素并非数量时的ABC分类。由计算广义加权海明距离得到的结果使传统的ABC分析法具有新的面貌,便于人们加强对象的控制和管理。

TW-Co-MFC:Two-Level Weighted Collaborative Fuzzy Clustering Based on Maximum Entropy for Multi-View Data

In recent years,multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications.Despite the significant advances in multi-view clustering,two challenges still need to be addressed,i.e.,how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering.In this study,we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering(TW-Co-MFC)approach to address the aforementioned issues.In TW-Co-MFC,a two-level weighting strategy is devised to measure the importance of views and features,and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency.Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering.Experiments on real-world datasets show the effectiveness of the proposed approach.

基于Fisher判别的改进WFCM分割算法

针对FCM算法对初始聚类中心、隶属度矩阵和噪声比较敏感且运算耗时的问题,提出一种椒盐噪声电路图像分割的WFCM改进算法。利用已有算法对椒盐噪声进行判断,粗略复原出原始图像直方图,找出波谷点作为图像的潜在分割点;根据Fisher判别思想构建确定初始聚类中心的最优化问题,计算改进WFCM算法的初始聚类中心;采用WFCM算法对非噪声点进行分割,得到其隶属度值;依据噪声点空间信息对其隶属度矩阵填值,获得整幅图像像素点的隶属度矩阵。仿真结果表明,改进算法能够更好地分割椒盐噪声电路图。

基于KFCMSA的(k,l)加权社交网络匿名算法

图数据隐私保护的研究目前主要集中在简单图,适应范围有限。将权重图数据的隐私保护作为研究对象,可以改善权重图发布之后数据的可用性及有效性。针对在利用聚类匿名化方法处理社交网络数据时,需要增删大量的边和节点,造成严重的数据失真的问题进行了研究。提出了(k,l)加权社交网络匿名算法KFCMSA(联合k成员模糊聚类和模拟退火),并利用改进的簇划分算法将权重社交网络聚类成不同的簇,对同一簇中节点的边权重进行泛化,使节点满足l多样性。在实现k度匿名的同时有效减少了边的改变量,提高了数据的可用性,实现最优聚类的同时防止了同质性攻击。聚类质量实验和数据可用性分析表明该算法具有较高的性能优势和较高的边保留率。

基于模糊灰色聚类法的植保无人机健康状态评估

为了预测和掌握植保无人机的健康状态,提出一种基于模糊灰色聚类法的植保无人机健康状态评估模型。实例表明:构建的评估模型可处理小样本、贫信息、少数据建模的问题,能够对植保无人机健康状态进行有效评估,降低设备故障率,具有很好的应用价值。

基于知识量加权的直觉模糊均值聚类方法

针对聚类算法中特征数据对聚类中心贡献的差异性及算法对初始聚类中心的敏感性等问题,提出一种基于知识量加权的直觉模糊均值聚类方法。首先将原始数据集直觉模糊化并改进最新的直觉模糊知识测度计算知识量,据此实现数据集特征加权,再利用核空间密度与核距离初始化聚类中心,以提高高维特征数据集的计算精度与聚类效率,最后基于类间样本距离与最小知识量原理建立聚类优化模型,得到最优迭代算法。基于UCI人工数据集的实验结果表明,所提方法较大程度地提高了聚类的准确性与迭代效率,分类正确率及执行效率分别平均提高了10.63%和31.75%,且具有良好的普适性和稳定性。该方法首次将知识测度新理论引入模糊聚类并取得优良效果,为该理论在其他相关领域的潜在应用开创了新例。