车内噪声声品质的支持向量机预测

对多元线性回归、神经网络和支持向量机的三个预测模型进行了研究。以车内噪声为例,建立了基于以上三种方法的车内噪声声品质预测模型,并采用留一法交叉检验作比较,所构建的支持向量机模型预测精度高于其他两种方法。实验结果同时也表明,支持向量计算法具有较强的稳健性和良好的泛化能力,能够用于车内噪声声品质的预测。     

不均衡数据下基于SVM的故障检测新算法

针对传统支持向量机(SVM)算法在数据不均衡情况下无法有效实现故障检测的不足,提出一种基于过抽样和代价敏感支持向量机相结合的故障检测新算法。该算法首先利用边界人工少数类过抽样技术(BSMOTE)实现训练样本的均衡。为减少人工增加样本带来的噪声影响,利用K近邻构造一个代价敏感的支持向量机(CSSVM)算法,利用每个样本的代价函数消除噪声样本对SVM算法分类精度的影响。将该算法应用在轴承故障检测中,并同传统的SVM算法,不同类代价敏感SVM-C算法,SVM和SMOTE相结合的算法进行比较,试验结果表明当样本不均衡时,建议算法的故障检测性能较其它算法有显著提高。     

A particle swarm‐optimized support vector machine for reliability prediction

System reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy. Copyright © 2011 John Wiley & Sons, Ltd.     

Fatigue life prediction for vibration isolation rubber based on parameter‐optimized support vector machine model

Given the small sample size, nonlinearity, and large dispersion of the measured data of fatigue performance for vibration isolation rubbers, the fatigue life prediction model for vibration isolation rubber materials was established using a support vector machine (SVM). A modified gravity search algorithm (MGSA) is proposed to optimize the parameters of the SVM. Using environmental temperature, the Rockwell hardness of the rubber compound, and the engineering strain peak as the input variables, the model was trained based on the experimental fatigue data of vibration isolation rubber materials. For comparison, the standard genetic algorithm, the standard particle swarm algorithm, and the standard simulated annealing algorithm are also implemented. Moreover, a back propagation neural network regression model is applied to the life prediction, with the conclusion that the prediction accuracy and the efficiency of MGSA are better than those of extant methods. This work can provide reference for further fatigue life prediction and structural improvement of rubber parts.     

冷壁面霜层生长的支持向量机模型

针对结霜过程因具有明显的非线性特征,采用传统方法难以精确预测的问题。建立了基于支持向量机的冷壁面霜成生长的预测模型,应用实验数据对模型进行验证、评估,并与基于最小二乘法的非线性多元回归模型进行了对比、分析。结果表明,基于支持向量机的预测模型能够很好的解决非线性预测问题。在已建立的预测模型基础上,以霜层生长过程中传热率预测为例,分别在测试集中的自变量与因变量加入不同噪声信号对模型预测性能影响进行了研究。结果表明,基于支持向量机的模型具有良好的抗干扰能力。     

Prediction of drug synergy score using ensemble based differential evolution

Prediction of drug synergy score is an ill‐posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression‐based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.Inspec keywords: cancer, evolutionary computation, support vector machines, regression analysis, drugs, learning (artificial intelligence), medical computingOther keywords: ensemble based differential evolution, specific cancer agents, efficient regression‐based machine learning technique, drug synergy prediction errors, efficient machine learning technique, drug synergy prediction technique, support vector machine, prediction precision, trial vector generation techniques, initial generation technique, drug synergy data, drug synergy score prediction, medical field, SVM kernel attributes, ensemble based DE, control attribute settings, competitive machine learning techniques, root mean square error     

基于Choquet模糊积分SVM集成及其实证研究

为了进一步提高SVM集成的泛化能力,提出了基于Choquet模糊积分的SVMs集成方法,综合考虑各个子SVM输出重要性,避免了现有SVM集成方法中忽略次要信息的问题。应用该方法,以高校的区域经济贡献度为例进行仿真试验,结果表明基于Choquet模糊积分的SVMs集成方法较基于Sugeno模糊积分SVMs集成方法和基于投票策略的SVMs集成方法具有更高的准确性。该方法是可行、有效的,具有一定的推广价值。     

基于GWO的SVM在红外甲烷传感器测量误差分析中的应用

针对传统支持向量机回归模型应用在红外甲烷传感器测量数据处理时出现预测精度低的问题,提出了一种基于灰狼优化算法的支持向量机回归模型。该模型在传统支持向量机的基础上,利用灰狼优化算法自适应搜索特征空间来选择最佳特征组合,经过循环比较,能快速、准确地搜索到最优的惩罚因子C与gamma参数。用实验室研制的红外甲烷传感器对0～5.05%浓度范围的标准甲烷气体进行测量后,建立了3种SVM回归模型,并进行对比。结果表明,采用灰狼优化算法建立的支持向量机回归模型其绝对误差和相对误差小,精度高。     

改进MDSMOTE与PSO-SVM在汽车组合仪表分类预测中的应用

汽车组合仪表生产过程中质检项目多且检测时间长,这在一定程度上制约了其生产效率的进一步提升。为此,提出一种基于改进最远点合成少数类过采样技术（max distance synthetic minority over-sampling technique,MDSMOTE）的支持向量机（support vector machine, SVM）分类预测方法。首先,结合专家经验对汽车组合仪表的原始生产数据进行特征筛选,并在MDSMOTE中引入类不平衡率I_R,以对所筛选的特征数据进行扩充;然后,利用粒子群优化（particle swarm optimization, PSO）算法对SVM的误差惩罚因子C和核函数参数γ进行优化;最后,建立优化的SVM分类预测模型,并对汽车组合仪表进行分类。通过与其他分类预测模型在不同数据集上的预测结果进行对比可知,基于改进MDSMOTE的SVM分类预测模型的准确率、F值和几何平均值等评价指标均优于其他模型。所提出方法在汽车仪表产品分类上表现出较强的泛化能力和稳定性,可为仪表制造企业生产效率的提升提供有效参考。     

基于粒子群算法优化支持向量机的铝热连轧机轧制力预报

为了提高热轧带材的轧制力预报精度,提出了粒子群算法和支持向量机结合的方法来预报轧制力。根据轧制原理用支持向量机建立轧制力预报的模型,通过粒子群算法优化支持向量机参数来提高预报精度。为了进一步提高轧制力预报精度,还提出了支持向量机网络与数学模型相结合的方法,对某“1+4”铝热连轧厂现场采集的5052铝合金轧制数据进行离线仿真,仿真结果可以看出支持向量机网络与数学模型结合的方法预报轧制力,提高了轧制力预报速度并使其轧制力预报精度控制在7%以内。     

Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.     

基于振动分布特征的电力变压器绕组故障诊断

振动分析法是实现电力变压器带电监测与故障诊断的重要手段,而基于振动分析法的故障诊断方法的关键在于从复杂的油箱壁振动信号中提取出状态特征(值或矢量)。传统的状态特征提取方法大多选取单个测点的振动信号进行时域或频域特征的提取,往往忽略了各测点间的振动分布特征。从振动重心的角度对振动分布的幅值重心及重心轨迹进行研究与分析,能够提出四个量化参数。在四个量化参数的基础上结合支持向量机分类算法提出基于振动分布特征的变压器绕组故障诊断模型。实际变压器的绕组故障实验以及十余台台电力变压器现场实测数据样本的分析与测试结果均表明,提出的振动分布特征及量化参数能够有效反映变压器绕组变形、压紧力松弛等机械结构变化,而基于振动分布特征的绕组故障诊断模型也可准确的对变压器绕组机械结构状态进行检测与诊断。     

面向电子商务的客户关系管理数据挖掘模型研究

客户资源是企业竞争力的归宿,客户关系需要进行科学管理,这已成为现代企业的共识。电子化的客户关系管理要求企业建立客户资源数据库,并基于客户资源数据库通过网络实现客户需求分析、挖掘客户资源、实现个性化客户服务等。本文对支持向量机理论进行了研究,在介绍SVM原理的基础上,给出了基于支持向量机理论的高维空间数据挖掘方法,并结合实例研究给出了面向电子商务的智能客户关系管理模型。     

基于EEMD和SVR的单自由度结构状态趋势预测

为了解决结构早期损伤难以正确识别的问题,本文结合聚类经验模式分解（EEMD）解决随机不确定性问题和支持向量机（SVM）解决预测问题这两者的优势,提出了一种基于EEMD特征提取的支持向量机回归（SVR）结构状态趋势预测方法。先对单自由度结构渐进损伤的加速度振动信号进行EEMD,再进行希尔伯特变换（HT）,计算瞬时频率,然后用回归支持向量机对反映结构健康状态的瞬时频率进行趋势预测。研究表明：对于渐变损伤该方法可以准确地、高精度地预测结构状态趋势。     

基于深度度量学习的轴承故障诊断方法

针对机械大数据因故障类内离散度和类间相似度较大而导致诊断精度低的问题,提出一种深度度量学习故障诊断方法,采用深度神经网络(Deep Neural Network, DNN)对故障特征进行自适应提取,并利用基于欧氏距离的边际Fisher分析(Marginal Fisher Analysis, MFA)方法进行了优选,在构建的深度度量网络(Deep Metric Network, DMN)顶层特征输出层添加BPNN(Back Propagation Neural Network, BPNN)分类器对网络参数进行微调,并实现故障的分类识别。通过对不同类型和严重程度的轴承故障进行了诊断分析,验证了该方法可以有效地对轴承故障进行高精度诊断,效果优于传统深度信念网络(Deep Belief Network, DBN)故障诊断方法以及常用时域统计特征结合支持向量机(Support Vector Machine, SVM)分类的故障诊断方法。     

基于VMD的铣刀破损检测

针对铣削过程中的切削振动信号具有非平稳性的特点,提出了一种基于变分模态分解(VMD)的铣刀破损检测方法。该方法通过VMD将切削振动信号分解成若干个模态分量,由于铣刀发生破损后,不同模态分量的频带分布会发生变化,因此提取各模态分量的中心频率和能量组成特征向量;对特征向量进行归一化处理,最终输入到支持向量机(SVM)进行铣刀破损检测。在多种切削参数下进行铣削加工实验,结果表明该方法比基于EMD的铣刀破损检测方法能抑制模态混叠的发生且具有更高的检测精度。     

基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断

针对传统支持向量机(SVM)算法在滚动轴承故障诊断领域中,对失衡数据集效果不佳、对噪声敏感以及对本身参数依赖较大等缺点,提出一种基于样本特性的过采样算法(OABSC)。该算法利用改进凝聚层次聚类将故障样本分成多个簇;在每个簇中综合考虑样本距离、近邻域密度对"疑似噪声点"进行识别、剔除,并将剩余样本按信息量进行排序;紧接着,在每个簇中采用K^*-信息量近邻域(K^*INN)过采样算法合成新样本,以使得数据集平衡;模拟3种不同失衡比下的轴承故障情况,并采用粒子群算法优化了SVM分类器的参数。经试验证明:相比已有算法,OABSC算法能更好地适用于数据呈多簇分布且失衡的轴承故障诊断领域,拥有更高的G-mean值与AUC值以及更强的算法鲁棒性。     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

基于VMD-DE的坦克行星变速箱故障诊断方法研究

为了提高坦克行星变速箱齿轮故障模式识别准确率,将变分模态分解(VMD)与散布熵(DE)结合提出故障特征提取新方法。利用波形法确定VMD分解层数,VMD分解振动信号得到一组固有模态分量(IMF);根据归一化互信息准则筛选若干IMF重构信号,计算重构信号的散布熵;将重构信号散布熵作为特征值输入到粒子群优化(PSO)的多分类支持向量机(SVM)中实现故障模式识别。通过对坦克行星变速箱的正常、行星轮故障和太阳轮故障三种状态进行模式识别,分类准确率达到100%,且计算时间较短。与基于原始振动信号DE、VMD-SE(样本熵)、VMD-PE(排列熵)及EMD-DE(经验模态分解与DE结合)等方法比较,综合考虑准确率和计算时间两个因素,基于VMD-DE的方法故障诊断性能最佳。