Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.

Least Squares One-Class Support Tensor Machine

One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

Forecasting of wind velocity:An improved SVM algorithm combined with simulated annealing

Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to the analysis with support vector machine method, the drawback of determining the parameters only by experts' experience should be improved. After a detailed description of the methodology of SVM and simulated annealing, an improved algorithm was proposed for the automatic optimization of parameters using SVM method. An example has proved that the proposed method can efficiently select the parameters of the SVM method. And by optimizing the parameters, the forecasting accuracy of the max wind velocity increases by 34.45%, which indicates that the new SASVM model improves the forecasting accuracy.

Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on Improved PSO and LSSVM

For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction model.Firstly,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate.Secondly,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying IPSO.Finally,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP model.The results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction models.The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.

A novel method for detection of hard exudates from fundus images based on SVM and improved FCM

Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.

An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval

Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.

One-Class Support Vector Machine with Relative Comparisons

One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative comparisons. The analysis uses a hypersphere version of one-class SVMs with a penalty term appended to the objective function. The method simultaneously finds the minimum sphere in the feature space that encloses most of the target points and considers the relative comparisons. The result is a standard convex quadratic programming problem, which can be solved by adapting standard methods for SVM training, i.e., sequential minimal optimization. This one-class SVM can be applied to semi-supervised clustering and multi-classification problems. Tests show that this method achieves higher accuracy and better generalization performance than previous SVMs.

ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION

One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.

Online power quality disturbance detection by support vector machine in smart meter

Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are applied.Considering the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data.This model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training time.In the case of existence,the disturbances are further classified into different types such as sag,swell,transients and unbalanced.Being light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring.The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

基于改进SKNet-SVM的网络安全态势评估

为提高网络安全态势评估的准确率,增强稳定性与鲁棒性,提出一种基于改进选择性卷积核卷积神经网络和支持向量机的网络安全态势评估模型。首先,使用改进选择性卷积核代替传统卷积核进行特征提取,提高卷积神经网络感受野变化的自适应性,增强特征之间关联性。然后,将提取的特征输入到支持向量机中进行分类,并使用网格优化算法对支持向量机中的参数进行全局寻优。最后,根据网络攻击影响指标计算网络安全态势值。实验表明,基于改进选择性卷积核卷积神经网络和支持向量机的态势评估模型与传统的卷积神经网络搭建的态势评估模型相比,准确率更高,并且具有更强的稳定性和鲁棒性。

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

基于EEMD-IGWO-SVM的电机轴承故障诊断

针对电机轴承易发生损坏、传统诊断方法耗时长且准确度低等问题,提出一种基于改进灰狼优化算法(IGWO)优化支持向量机(SVM)的电机轴承故障诊断方法。对电机振动数据进行集成经验模态分解(EEMD),提取出IMF能量矩作为特征向量,并结合IGWO-SVM分类器,构造电机轴承故障检测模型。在模型引入改进Tent混沌映射、非线性收敛因子、动态权重策略,得到改进的分类算法,该算法可以快速精准地寻找SVM的最优惩罚参数C和核参数γ。对电机轴承振动数据进行仿真实验,诊断结果表明该轴承故障方法平均准确率高达99.4%。最后通过实验验证提出的诊断方法具有良好的算法稳定性和抗噪性能,可有效提高故障诊断精度。

采用改进遗传算法优化LS-SVM逆系统的外转子无铁心无轴承永磁同步发电机解耦控制

为了实现外转子无铁心无轴承永磁同步发电机(outer rotor coreless bearingless permanent magnet synchronous generator,ORC-BPMSG)的精确控制,提出一种基于改进遗传算法(improved genetic algorithm,IGA)优化最小二乘支持向量机(least square support vector machine,LS-SVM)逆系统的解耦控制策略。首先,基于ORC-BPMSG的结构及工作原理,推导其数学模型,并分析其可逆性。其次,建立LS-SVM回归方程,并采用IGA优化LS-SVM的性能参数,从而训练得到逆系统。然后,将逆系统与原系统串接,形成伪线性系统,实现了ORC-BPMSG的线性化和解耦。最后,将提出的控制方法与传统LS-SVM逆系统控制方法进行对比仿真和实验。仿真和实验结果表明:所提出的控制策略可以较好地实现ORC-BPMSG输出电压和悬浮力、以及悬浮力之间的解耦控制。

参数优化的IZOA-SVM机械设备故障诊断方法

在复杂的工作环境中,机械设备振动信号的复杂性常常会导致机械设备故障诊断的准确性不高,为解决设备运行中因信号复杂性引发的故障诊断难题,提出了一种参数优化的斑马优化算法优化支持向量机(IZOA-SVM)的故障诊断方法。首先,引入了柯西变异和反向学习的改进策略到斑马优化算法(ZOA)中,提出了改进的斑马优化算法(IZOA),旨在改善原有斑马优化算法在迭代后期容易陷入局部极值等问题,从而有效增强了其全局搜索能力;其次,利用IZOA优化支持向量机(SVM)的核参数g和惩罚参数c以寻找SVM最优参数组合[c,g],并构建了IZOA-SVM模型;然后,计算了样本的13个时域特征以构成特征向量,并将特征向量分别输入到IZOA-SVM模型、斑马优化算法优化支持向量机(ZOA-SVM)模型、粒子群算法优化支持向量机(PSO-SVM)模型、遗传算法优化支持向量机(GA-SVM)模型和支持向量机模型,进行了故障分类;最后,通过旋转机械振动及故障模拟试验验证了该方法的有效性。研究结果表明:IZOA-SVM模型在分类准确率方面得到了明显的提高,达到了98.33%;该模型能够精准而稳定地识别故障类型,提高故障识别的准确性,在准确率方面相较于其他对比方法表现出更为显著的优势。因此,该方法在全局搜索和故障分类准确性方面都取得了明显的改进,为复杂环境下的故障诊断提供了可参考的解决方案。

基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断

目的为了解决双向DC-DC电力变换器的软故障诊断精度不高的问题,方法提出基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型。首先,分析双向DC-DC电力变换器中电容、电感和MOSFET管的故障机理,通过仿真实验模拟各元件失效后变换器的输出电气参数变化,从而确定变换器不同元件故障时对应的故障特征参数;其次,构建改进的LSTM-SVM双向DC-DC电力变换器故障诊断组合模型,在LSTM中添加Mogrifier门机制,提高LSTM提取时间序列原始数据中微弱特征的能力;最后,由于传统LSTM的末端分类器为Softmax,其主要解决单一元件诊断问题,变换器故障类型较多,维数较高,所以采用麻雀搜索算法优化的SVM代替原有的Softmax函数,对LSTM输出的数据进行故障分类,提高故障诊断的准确率。设置双向DC-DC电力变换器充放电两种状态下,包含电解电容、电感和MOSFET单双管故障在内的24组故障,分别采用本文构建的改进的LSTM-SVM和原始的LSTM-SVM双向DC-DC变换器故障诊断模型进行诊断。结果结果表明,改进的LSTM-SVM故障诊断模型诊断准确率平均值为99.71%,原始的LSTM-SVM故障诊断模型诊断准确率平均值为88.48%,改进的LSTM-SVM故障诊断模型对各元件的故障诊断正确率均高于原始的LSTM-SVM故障诊断模型的。结论基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型实现了对双向DC-DC电力变换器中的电解电容、电感和MOSFET单双管故障的准确诊断。

电动汽车永磁同步电机匝间短路检测算法仿真

当电机出现匝间短路故障时,电机内部电阻会大幅度降低,电流突增,造成其余元件功率过大问题。若不能及时监测该故障电机将会导致二次损坏。但是,由于短路故障具有瞬时性,其特征获取难度较大。为此提出电动汽车永磁同步电机匝间短路故障检测方法。构建驱动汽车的永磁同步电机模型,依据当永磁同步电机模型处于匝间短路故障状态时,基波电流与正序电流的制约关系失效的原理,提取电机匝间短路故障特征。基于此利用粒子群算法-最小二乘支持向量机(Particle swarm optimization-Least Squares Support Vector Machine,PSO-LSSVM)获取故障检测结果,实现电动汽车永磁同步电机匝间短路故障的检测。实验结果表明,研究方法在任意时刻检测到的负载力矩均与实际值吻合,且输出的电机残余能量具有较高可靠性,说明了上述方法具有较强的可应用性。

面向造纸污水处理的故障诊断复合算法研究

故障诊断是保障系统稳定性与安全性的关键节点。在造纸污水处理过程中,系统硬件设备由于长期处于恶劣环境极易引发系统故障,因此准确诊断故障以避免不可挽回的损失至关重要。基于此,针对造纸污水处理过程的特点,以主成分分析技术提取故障主元而明确故障诊断模型输入量,以粒子群优化算法优化机器学习算法支持向量机而构成故障诊断复合算法,由此搭建了面向造纸污水处理的故障诊断模型,并进行了仿真分析。结果发现,面向造纸污水处理的故障诊断复合算法正确率可达96.9%,且稳定性与鲁棒性较高,可广泛推广至多工业污水处理领域。

基于IPOA-LSSVM模型的高压直流输电线路故障定位

故障定位在长距离高压直流输电系统中起着至关重要的作用.针对线路衰减系数计算不准和二次波头难以捕捉的问题,提出了一种改进鹈鹕优化算法(IPOA)优化最小二乘支持向量(LSSVM)的故障定位模型.根据行波衰减原理,推导故障距离和线路两端线模分量模极大值比的计算公式,发现二者具有非线性关系.使用LSSVM泛化二者之间的关系,将改进后的POA算法对LSSVM的关键参数进行寻优,建立IPOA-LSSVM故障定位模型.通过在两端采集故障信号,对其进行小波变换得到首波头幅值比作为模型的输入量,故障距离作为输出量进行仿真验证.仿真结果表明,该模型不受过渡电阻和故障类型的影响,能够可靠准确地定位.

基于IDBO-LSSVM的输电线路覆冰厚度预测模型

针对输电线路受多种气象因素影响导致覆冰厚度预测精度低的问题,提出基于改进蜣螂优化(improved dung beetle optimizer,IDBO)算法优化最小二乘支持向量机(least square support vector machine,LSSVM)的输电线路覆冰厚度预测模型。首先,使用皮尔逊相关系数(Pearson correlation coefficient,PCC)计算输电线路覆冰厚度与不同气象因素之间的相关性,选择具有高相关性的气象因素以确定输入变量;其次,通过引入Halton序列、Levy飞行策略和T分布扰动来改进蜣螂优化(dung beetle optimizer,DBO)算法;最后,使用IDBO算法寻优LSSVM参数:调节因子、核函数宽度,提高模型预测精度。以某地输电线路历史监测数据为样本,将IDBO-LSSVM的输电线路预测结果与其他7种预测模型进行比较,发现平均绝对误差分别降低了约27%、36%、25%、23%、24%、44%和39%。该研究证实了基于IDBO-LSSVM的输电线路覆冰厚度预测模型可以有效提高预测精度。