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.

Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland

Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem.

Future changes in rainfall, temperature and reference evapotranspiration in the central India by least square support vector machine

Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotrans- piration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ETo is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ETo have been shown by downscaling the HadCM3 （Hadley Centre Coupled Model version 3） model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ETo and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine （LS-SVM） model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ETo. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081-2091 and 2091-2099 in maximum temperature and 2091-2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ETo in some decades. Two peaks of the increase are observed in ETo in the April-May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.

Credit scoring by feature-weighted support vector machines

Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.

Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision

Apostichopus japonicus(Holothuroidea,Echinodermata) is an ecological and economic species in East Asia.Conventional biometric monitoring method includes diving for samples and weighing above water,with highly variable in weight measurement due to variation in the quantity of water in the respiratory tree and intestinal content of this species.Recently,video survey method has been applied widely in biometric detection on underwater benthos.However,because of the high flexibility of A.japonicus body,video survey method of monitoring is less used in sea cucumber.In this study,we designed a model to evaluate the wet weight of A.japonicus,using machine vision technology combined with a support vector machine(SVM) that can be used infield surveys on the A.japonicus population.Continuous dorsal images of free-moving A.japonicus individuals in seawater were captured,which also allows for the development of images of the core body edge as well as thorn segmentation.Parameters that include body length,body breadth,perimeter and area,were extracted from the core body edge images and used in SVM regression,to predict the weight of A.japonicus and for comparison with a power model.Results indicate that the use of SVM for predicting the weight of 33 A.japonicus individuals is accurate(R^2=0.99) and compatible with the power model(R^2=0.96).The image-based analysis and size-weight regression models in this study may be useful in body weight evaluation of A.japonicus in lab and field study.

An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.

基于ICEEMDAN和时变权重集成预测模型的变压器油中溶解气体含量预测

为了实现对变压器油中溶解气体体积分数的精确预测,同时克服仅使用单一预测模型导致预测精度及泛化能力不足的局限,提出了一种基于改进完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition,ICEEMDAN)和灰色关联系数时变权重集成预测模型的变压器油中溶解气体预测方法。首先将溶解气体含量序列模态分解为一系列具有不同时间尺度的子序列。然后,使用门控循环神经网络和麻雀搜索算法优化支持向量机对各子序列进行训练,组合为一个集成预测模型;并比较不同预测方法的预测精度,计算灰色关联系数时变权重,形成各子系列的预测结果。最后将各子序列的预测结果叠加重构,得到最终预测结果。算例分析结果显示:该方法单步预测的均方根误差、平均绝对误差和相关系数分别为0.593、0.422和0.768,相比其他算法在预测精度上有明显提升,同时具有很强的泛化性能,可以为油浸式变压器内部状态监测提供依据。

纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析

背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(P<0.05)。④富集分析结果提示,纤维肌痛综合征的富集途径包括9条,主要与嗅觉传导、神经活性配体-受体相互作用及感染等通路密切相关。⑤上述结果显示,纤维肌痛综合征的发生发展与多基因参与、免疫调节异常及多个通路失调有关,但这些基因与免疫细胞之间的相互作用,以及它们与各通路之间的关系尚需进一步研究。

融合多特征信息与GWO-SVM的机械关键设备故障诊断

为了提高机械关键设备故障诊断的精度,建立机械关键设备故障诊断模型。文章提出一种融合机械关键设备故障信号多特征信息与灰狼优化算法(Grey Wolf Optimization Algorithm,GWO)改进支持向量机(Support Vector Machine,SVM)(GWO-SVM)的机械关键设备故障诊断模型。首先,提取机械关键设备故障信号的时域特征、频域特征和多尺度加权排列熵特征,分别对比不同特征的机械关键设备故障诊断结果。其次,为提高SVM模型性能,运用GWO算法对SVM模型的惩罚参数P和核函数参数g进行优化选择,提出一种融合多特征信息与GWO-SVM的机械设备故障诊断模型。与GA-SVM、PSO-SVM和SVM相比,基于GWO-SVM的机械设备故障诊断模型的诊断精度最高。这里算法可以有效提高机械关键设备故障诊断正确率,为机械关键设备故障诊断提供了新的方法。

基于特征判定系数的电力变压器振动信号故障诊断

变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义。针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法。通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断。对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率。

Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine

In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.

基于特征加权混合隶属度的模糊孪生支持向量机

模糊孪生支持向量机(FTSVM)忽略了不同特征间的差异,导致核函数或距离的计算无法准确反映样本间的相似性,使FTSVM在处理含有大量不相关或弱相关特征的高维数据分类时,难以达到良好分类效果;且隶属度的设计未有效区分离群点或噪声。针对以上问题,提出了一种基于特征加权混合隶属度的FM-FTSVM。首先计算每个特征的信息增益,并依据信息增益值的大小为特征赋予权重,降低不相关或弱相关特征的作用,使其能更好地应用于高维数据分类;然后,为每一类样本构造一个最小包围球计算基于紧密度的特征加权隶属度,并结合基于距离的特征加权隶属度得到特征加权混合隶属度,综合考虑样本点到类中心的特征加权欧式距离和样本间的紧密程度,可更好识别离群点或噪声数据;最后,融合特征加权核函数,降低不相关特征对核函数或距离计算产生的影响。与对比算法在人工数据集、高维数据集和UCI数据集上进行比较,发现本文提出的方法在区分离群点、噪声和有效样本上有明显优势,且在高维数据集上可获得更好分类效果。

基于加权LS-SVM的青霉素发酵过程建模

青霉素发酵过程中,一些重要参数的检测存在一定的误差,给生产过程的监测及重要参数的实时监控等带来一定困难。样本数据中自变量、因变量均有可能包含误差数据,影响模型建立的准确性,本文采用加权最小二乘算法,给各个样本的误差平方赋予不同权重用于克服异常训练样本的影响,利用Pensim仿真平台数据,采用粒子群算法(PSO)对加权最小二乘向量机算法(WLS-SVM)的参数寻优,建立青霉素发酵过程模型,通过仿真实验表明了该算法用于青霉素发酵过程建模的有效性。

基于MPSO-WLS-SVM的矿井瓦斯涌出量预测模型研究

为有效预防瓦斯灾害,以预测矿井瓦斯涌出量为研究目的,提出经改进的粒子群算法(MPSO)优化的加权最小二乘支持向量机(WLS-SVM),并用其预测非线性动态瓦斯涌出量。算法通过对WLS-SVM的正则化参数C和高斯核参数σ寻优,建立基于MPSO优化的WLS-SVM的瓦斯涌出量预测模型,并利用某矿井监测到的各项历史数据进行实例分析。试验结果表明:该预测模型预测的最大相对误差为5.99%,最小相对误差为0.43%,平均相对误差为2.95%,较其他预测模型有更强的泛化能力和更高的预测精度。

中国冬季降水的支持向量机预测模型研究

我国冬季降水对于农业、水资源管理和自然灾害风险评估具有重要意义.受多种气象因素的影响,冬季降水的预测仍具有挑战性,进一步提升冬季降水的预测技巧是当下短期气候预测研究的重要课题.本研究采用支持向量机(SVM)方法,旨在通过机器学习方法提高中国冬季降水的预测准确率.基于NCEP_CFS, ECMWF_SYSTEM, BCC_CSM等五个模式数据以及站点数据,建立针对冬季降水的SVM集成预测模型,并与单个模式和等权集合平均模型(AVE)加以对比.SVM模型因其强泛化和处理非线性问题的能力,在中国冬季降水预测中表现良好.研究表明:(1)SVM模型较单个模式及AVE模型的预测准确性与稳定性得到大幅提升,SVM模型的PS评分和PCS评分显著高于单个成员模式的结果,最大分别提高了8.0(12.6%)和3.9(7.4%),较AVE模型则最大分别提高了5.4(8.2%)和2.1(3.8%),预报技巧的提高在观测资料相对缺乏的西南和西北地区尤为明显.(2)从均方根误差和时间相关系数的空间分布上来看,SVM模型对其成员模式在西藏地区、西南地区、华东及华南地区误差较大的情况改善明显,误差最大降低了259(90.9%),预报技巧最大提高了1.13.(3)独立样本检验中,SVM模型的PS评分和PCS评分显著高于单个模式和AVE模型,最大提高了10.79(20.3%)和11.39(27.3%).因此,SVM模型的构建,将有助于进一步提高中国冬季降水预测的准确性和稳定性,为气象防灾减灾和气候资源开发利用等提供重要技术支撑.

基于提升小波和LS-SVM的大坝变形预测

提出了一种基于提升小波和最小二乘支持向量机的大坝变形预测方法,通过提升小波分析提取大坝监测数据效应量,分别对各效应量使用最小二乘支持向量机模型进行训练预测,再将合成各分量的预测结果作为最终的变形预测结果。算例结果表明,该方法较符合实际情况,具有很高的预测精度和良好的泛化能力。

基于相似日和蚁群优化LS-SVM的短期电力负荷预测

由统计学习理论发展的通用学习方法——支持向量机,在解决小样本、非线性及高维数等问题中表现出许多特有的优势。提出了采用最小二乘支持向量机建立负荷预测模型,它是对标准的支持向量机的一种扩展,降低了问题的复杂性,使得计算速度相对加快。在选取最小二乘支持向量机的训练样本时,采用加权的灰色关联度方法来选择相似日,对不同样本根据其重要性赋予不同的权重,同传统的关联度相比更具客观性。另外,对于最小二乘支持向量机的参数选择问题,针对目前尚无统一有效方法的现状,尝试采用了一种基于蚁群种群的新型优化算法———蚁群算法来优化选择,在很大程度上减少了人为选择参数的主观影响。最后通过实例验证了该模型的有效性,取得了比较满意的预测效果。

基于加权LS-SVM的闭环控制系统故障诊断研究

针对闭环控制系统故障诊断难的问题,利用加权最小二乘支持向量机(Weighted Least Squares Support Vector Machines,Weighted LS-SVM)设计了一个闭环控制系统的故障诊断系统。仿真试验结果表明,加权LS-SVM具有很高的建模精度、较强的泛化能力和很高的故障诊断准确度。并验证了该方法的有效性和先进性。