支持向量机应用于大气污染物浓度预测

支持向量机是基于统计学习理论的新一代机器学习技术,其非线性回归预测性能优越于传统统计方法。提出了一种大气污染物浓度预测模型,该方法将支持向量机应用于大气污染物浓度预测,首先对各类影响因子进行分析并进行建模预测;而后利用主成分分析的方法对输入因子降维,从而形成支持向量机的训练样本集;在此基础上建立了基于RBF核函数支持向量回归法的大气污染预模型。大气污染预测实例表明,该方法具有泛化能力强、预测精度高、训练速度快、稳定性好、便于建模等优点,有良好的应用前景。     

支持向量机应用于大气污染物时间序列预测

阐述了支持向量机应用于大气污染物时间序列预测的具体方法,建立了大气污染物时间序列的支持向量机预测模型.该方法将支持向量杌应用于大气污染物浓度预测:首先通过选择合适的信息量准则来确定模型阶数:而后通过实验的方法选择参数从而形成支持向量机的训练样本集,在此基础上建立了基于支持向量机的时间序列大气污染预测模型.实例表明,无论是在仿真过程还是在预测过程,支持向量机都具有很高的预测精度.因此.采用支持向量机方法对大气污染物时间序列进行预测分析是可行的.     

工业大气污染物浓度的复合自回归网络预测

针对工业园区大气污染管理中预测能力较弱的问题,考虑工业大气污染物的多因素耦合及非线性时序特征,提出一种工业大气污染物浓度预测方法。根据预测指标数值特征,提出复合自回归神经网络（CNAR）。对目标预测指标及影响因素进行关联分析及时序建模,实现对工业大气污染物浓度的短期预测。选用河北省某市大气网格化监测数据进行模型训练与方法验证,实验结果表明CNAR预测模型可对工业大气污染物浓度进行有效预测,效果优于传统自回归神经网络,为工业大气污染防控提供参考依据。     

公路旅游客流量预测的支持向量回归模型

介绍了基于统计学习理论的支持向量机回归原理,为解决公路旅游客流量预测建模中的小样本问题,实现对公路旅游客流量的快速准确预测,提出了基于支持向量机回归模型的公路旅游客流量预测方法,给出了参数优化选取算法。仿真实验表明,该方法具有比神经网络等方法更好的预测精度。说明支持向量回归方法用于公路旅游客流量预测是可行有效的。     

基于支持向量机的复杂时间序列预测研究

介绍了相空间重构和基于支持向量机的时间序列预测建模技术,提出了复杂时间序列的多尺度分解方法,对支持向量机回归与预测的各项参数设置进行了试验分析。对股票数据进行建模和预测,结果表明支持向量机对复杂时问序列具有较好的预测效果。     

加权因子的PSO-SVR区域空气PM2.5浓度预报方法

结合支持向量回归机(SVR)和粒子群优化算法(PSO),本文提出了一种加权因子的区域大气PM2.5浓度预测方法(W-PSO-SVR)。该方法对预测模型的输入变量进行[0,1]间的不平等加权赋值,权重值由PSO寻优求得,通过不断寻优迭代,赋予输入变量不平等的权重,从而建立预测模型。对区域大气PM2.5浓度预报结果表明,与单独的支持向量回归机模型和0或1的加权因子的支持向量回归模型相比,W-PSO-SVR预报精度提高明显,且该方法不用考虑对历史数据时滞因子的影响,较好地实现了模型输入参数的有效选择,从而可降低输入参数的维数。     

基于小波和支持向量机的多尺度时间序列预测

介绍了相空间重构和基于支持向量机的时间序列预测建模技术,提出了基于小波和支持向量机的复杂时间序列预测方法,利用小波对复杂时间序列进行多尺度分解,对重构后的近似序列和细节序列分别利用支持向量机进行回归预测并将结果融合。对股票数据进行预测,试验结果表明该方法预测精度高于单尺度支持向量机和神经网络预测方法,可用于复杂非平稳时间序列的预测。     

基于粒子群优化的支持向量机在瓦斯浓度预测中的应用研究

为了准确预测煤矿瓦斯浓度,基于从芦岭煤矿KJ98监控系统中提取的生产现场瓦斯浓度时间序列数据,对基于粒子群优化的支持向量机理论在瓦斯浓度短期预测中的应用进行了研究。首先对瓦斯浓度时间序列进行小波软阈值去噪和相空间重构等预处理,然后采用粒子群优化算法对支持向量机的惩罚因子、损失函数、核函数参数进行了优化,并基于最优参数建立了瓦斯浓度预测的支持向量机模型。仿真结果表明,采用粒子群优化的支持向量机理论进行煤矿瓦斯浓度预测,极大地提高了预测的准确性和精确度;误差分析结果表明,该方法预测结果的误差很小,且测试样本越小,误差越小。     

加权相似度及加权支持向量机的短期负荷预测

短期电力负荷预测中,针对维数比较高、各影响因素差异大、随机误差差异性大等问题,提出一种基于加权相似度和加权支持向量机的模型。首先,通过主成分分析得到负荷数据的综合因子,利用灰色关联分析分析综合因子与各影响因素的关系,计算各个影响因素的权重;其次依据权重采用加权相似度公式获得相似日,即样本数据;最后,针对相似日,采用加权支持向量回归机进行建模,实现对短期电力负荷进行预测。实验结果表明了该方法的有效性。     

支持向量机在GDP回归预测中的应用研究

支持向量机是一种新的回归方法,介绍了基于支持向量机的回归建模技术,并应用于GDP的回归预测。GDP属性子集的特点是训练数据量比较少、含有稀疏数据。在转换、添加、下钻GDP相关属性的情况下对支持向量机的参数进行实验分析。实验结果显示支持向量机能很好的处理属性集的变化并得到很好的预测效果。     

Tribilogical performances of connecting rod and by using orthogonal experiment,regression method and response surface methodology

Dynamic lubrication analysis of connecting rod is a very complex problem. Some factors have great effect on lubrication, such as clearance, oil viscosity, oil supplying hole, bearing elastic modulus, surface roughness, oil supplying pressure and engine speed and bearing width. In this paper, ten indexes are used as the input parameters to evaluate the bearing performances: minimum oil film thickness (MOFT), friction loss, the maximum oil film pressure (MOFP) and average of the oil leakages (OLK). Two orthogonal experiments are combined to identify the factors dominating the bearing behavior. The stepwise regression is used to establish the regression model without insignificant variables, and two most important variables are used as the input to carry out the surface response analysis for each model. At last, the support vector machine (SVM) is used to identify the asperity contact. Compared with SVM model, the particle swarm optimization-support vector machines (PSO–SVM) can predict the asperity contact more precise, especially to the samples near dividing line. In future work, more soft computing methods with statistical characteristic are used to the tribology analyses.     

Fuzzy functions with support vector machines

A new fuzzy system modeling (FSM) approach that identifies the fuzzy functions using support vector machines (SVM) is proposed. This new approach is structurally different from the fuzzy rule base approaches and fuzzy regression methods. It is a new alternate version of the earlier FSM with fuzzy functions approaches. SVM is applied to determine the support vectors for each fuzzy cluster obtained by fuzzy c-means (FCM) clustering algorithm. Original input variables, the membership values obtained from the FCM together with their transformations form a new augmented set of input variables. The performance of the proposed system modeling approach is compared to previous fuzzy functions approaches, standard SVM, LSE methods using an artificial sparse dataset and a real-life non-sparse dataset. The results indicate that the proposed fuzzy functions with support vector machines approach is a feasible and stable method for regression problems and results in higher performances than the classical statistical methods.     

Change point determination for a multivariate process using a two-stage hybrid scheme

Effective identification of the change point of a multivariate process is an important research issue since it is associated with the determination of assignable causes which may seriously affect the underlying process. Most existing studies either use the maximum likelihood estimator (MLE) method or the machine learning (ML) method to estimate or identify the change point of a process. Typically, the MLE method may be criticized for its assumption that the process distribution is known, and the ML method may have the deficiency of using a large number of input variables in the modeling procedure. Diverging from existing approaches, this study proposes an integrated hybrid scheme to mitigate the difficulties of the MLE and ML methods. The proposed scheme includes four components: the logistic regression (LR) model, the multivariate adaptive regression splines (MARS) model, the support vector machine (SVM) classifier and the change point identification strategy. It performs three tasks in order to effectively identify the change point in a multivariate process. The initial task is to use the LR and MARS models to reduce and refine the whole set of input or explanatory variables. The remaining variables are then served as input variables to the SVM in the second task. The last task is to integrate use of the SVM outputs with our proposed identification strategy to determine the change point in a multivariate process. Experimental simulation results reveal that the proposed hybrid scheme is able to effectively identify the change point and outperform the typical statistical process control (SPC) chart alone and the single stage SVM methods.     

基于SVM的混沌时间序列分析

支持向量机是一种基于统计学习理论的新的机器学习方法,该方法已用于解决模式分类问题．本文将支持向量机（SVM）用于混沌时间序列分析,实验数据采用典型地Mackey-Glass混沌时间序列,先对混沌时间序列进行支持向量回归实验;然后采用局域法多步预报模型,利用支持向量机对混沌时间序列进行预测．仿真实验表明,利用支持向量机可以较准确地预测混沌时间序列的变化趋势．     

基于SVR的混沌时间序列预测

支持向量机是一种基于统计学习理论的新颖的机器学习方法,由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点。这种方法已广泛用于解决分类和回归问题。论文介绍了支持向量回归算法的各种版本,同时将它们应用到混沌时间序列预测中,并且比较了它们的预测性能,为实际应用合理选择模型提供一定的依据。     

基于核岭回归估计器的常压塔航煤干点推断控制

提出了一种基于核岭回归推断估计器的新型推断控制策略，来实现常压塔航煤干点的在线检测和控制.首先，对支持向量机与最小二乘支持向量机回归算法进行了分析，并提出一种直接优化核岭回归算法.其次，通过采集的二次变量数据和化验数据，用核岭回归方法建立了航煤干点的估计器模型．最后进行了仿真，结果表明，在相同样本集下，与支持向量机、RBF网络模型比较，所提建模方法调节参数少，预测精度高．     

基于线性规划支持向量回归的混沌系统预测

支持向量机是一种基于统计学习理论的新颖的机器学习方法,该方法已经广泛用于解决分类与回归问题。标准的支持向量机算法需要解一个二次规划问题,当训练样本较多时,其运算速度一般很慢。为了提高运算速度,介绍了一种基于线性规划的支持向量回归算法,并由此提出几种新的回归模型,同时将它们应用到混沌时间序列预测中,并比较了它们的预测性能。在实际应用中,可以根据具体情况灵活地选择所需模型。     

A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box

The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.     

An hybrid detection system of control chart patterns using cascaded SVM and neural network�Cbased detector

Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.     

基于SVR的异常数据检测

支持向量机是一种基于统计学习理论的新颖的机器学习方法,该方法已广泛用于解决分类和回归问题。该文利用支持向量回归算法中结构风险函数的性质以及KT条件,提出一种回归中的异常值检测方法。仿真实验结果表明了所给方法的可行性和有效性。