基于LS-SVM的福房价格指数预测

提出一种基于最小二乘支持向量机(LS-SVM)的福房指数预测方法.采用感知机核函数、多项式核函数和高斯核函数进行仿真模拟,经过参数选优建立了精度较高的福房指数预测模型.预测结果表明,利用LS-SVM模型进行预测具有误差小、拟合程度高等优点,可适用于房地产价格指数的预测.     

LS-SVM model based nonlinear predictive control for MCFC system

This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.     

基于支持向量机的铁路客运量预测

提出了一种基于最小二乘支持向量机(LS-SVM)的铁路客运量预测的新方法。1985—2002年的铁路客运量组成整个数据集。前5年的客运量用来预测第6年的客运量,由1985—1999年的客运量建立LS-SVM客运量预测模型。运用建立该模型预测2000—2002年的铁路客运量。结果表明:提出的LS-SVM客运量预测方法是有效的。     

基于粒子群优化算法的LS-SVM字符识别模型

提出一种基于粒子群优化算法优化相关参数的最小二乘支持向量机(LS-SVM)的字符识别模型.利用相关的字符数据,分别使用本方法和基于网格搜索的最小二乘支持向量机方法进行识别.仿真结果表明,该方法的精确度高于其它两种方法.     

基于LS-SVM的基坑周边地表沉降预测模型

针对神经网络用于基坑变形预测存在结构难确定、训练易陷入局部最优及易过学习等问题,以已有的周边地表沉降为样本,利用最小二乘支持向量机(LS-SVM)建立基坑地表沉降预测模型,应用网格搜索算法优化模型参数,对基坑周边地表沉降进行连续滚动的多步预测。实例结果表明,LS-SVM用于基坑周边地表沉降预测效果较好,具有所需数据少、推广能力强等优点。     

基于EEMD和LS-SVM的癫痫脑电信号识别

针对癫痫脑电(EEG)信号的非平稳性和非线性,提出一种基于集合经验模式分解(EEMD)提特征并利用最小二乘支持向量机(LS-SVM)的脑电信号分类方法。首先利用EEMD将EEG信号分成多个经验模式分量,得到各阶本征模式分量(IMF),然后提取有效特征,最后用LS-SVM对其进行分类,实验结果表明,该方法对癫痫发作间歇期和发作期EEG的提特征后分类识别正确率达到98%。     

基于小波核的LS—SVM算法在城市交通流预测中与其他核函数对比研究

针对城市交通流短时流量预测在智能交通系统中的重要性,在以往研究基础上采用Morlet函数作为小波核函数,进一步提高了模型的预测精度及泛化能力．将其与其他几种常用核函数模型进行比较,其效果明显优于其他核函数模型,能够满足智能交通控制和诱导的要求．     

基于Logistic映射相空间重构神经网络短期风电预测

针对风速有很强的混沌特性,采用一种相空间重构理论短期预测方法,确定风速的最佳延迟时间和嵌入维数,然后对样本空间进行重构,使用BP神经网络进行短期风速预测。实验结果表明,该模型可以较高的得到短期发电功率预测精度。     

基于LS-SVM的高速列车广义非线性模型子空间辨识

针对高速列车动力建模问题,提出了基于最小二乘支持向量机(LS-SVM)的高速列车广义非线性模型子空间辨识方法。先给出描述高速列车单质点力学行为的随机离散非线性状态空间模型,并进一步构建了高速列车广义非线性模型;采用LSSVM回归方法构造广义非线性函数,并运用子空间辨识方法,直接由增广输入、输出数据得到高速列车广义非线性模型参数矩阵。最后对上述模型进行了数值仿真。结果表明:所提出的基于LS-SVM的子空间辨识方法比常规LS-SVM方法、线性子空间方法对列车模型具有更高的预报性能,用于高速列车的建模是有效的,可用于具有非线性、强耦合的高速列车运行过程数学模型的辨识。     

基于MQPSO-LSSVM的微博热点话题预测

微博热点话题预测是一类小样本、不确定性的复杂预测问题,传统线性方法不能刻画微博热点话题的变化规律,神经网络存在过拟合、泛化能力不强等缺陷.为了提高微博热点话题的预测精度,提出了一种改进量子粒子群(QPSO)算法优化LSSVM的微博热点话题预测模型(MQPSO-LSSVM).首先采用MQPSO算法优化LSSVM的参数,然后将优化后的LSSVM对微博热点话题变化趋势进行建模,最后选取具体微博热点话题数据进行仿真实验.实验结果表明,MQPSO-LSSVM提高了微博热点话题的预测精度,预测结果具有一定实用价值.     

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality （PQ） combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction （PSR）, a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines （SVMs） are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term disturbances （voltage sags, swells） and long-term disturbances （flickers, harmonics）, as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform （WT） and Artificial Neural Network （ANN） are also reported to show its advantages.     

Degradation process of coated tinplate by phase space reconstruction theory

The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.     

基于投影矩阵的空间非合作目标三维重建

针对从摄像机运动恢复结构算法(structure from motion algorithm,SFM)重建结果精度低以及面片重建算法(patchbased multiview stereo,PMVS)计算时间长的缺点,提出1种基于投影矩阵空间非合作目标三维重建算法。首先采用针孔模型对三维空间点到二维图像像素点投影关系进行理论推导,然后利用对极几何约束原理求解出旋转矩阵和平移矩阵;再由投影矩阵,解算三维空间点坐标;最后结合精密可控转台,搭载双目相机进行仿真实验。仿真实验结果表明,与SFM算法和PMVS对比,重建结果点云个数分别增加了22倍和9倍,所需时间分别减少了5s和97s;验证该算法的高可靠性、高重建精度和强实时性。     

基于最小二乘支持向量机的故障电机诊断方法

由于从电机的频谱无法区别出故障电机,因而使用CZT变换(线性调频Z变换)分析采集到的电机数据,判断出电机是否有故障,对电机进行了分类;把采集到的数据分类后训练最小二乘向量机,再把相同维数的数据送入训练好的最小二乘向量机进行判断,最终得出用最小二乘向量机进行电机的故障诊断的准确性,从而说明了用最小二乘向量机进行故障诊断的可行性和可靠性。     

Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic(sEMG) signals. In contrast to the existing methods,considering the non-stationary and nonlinear characteristics of EMG signals,to get the more separable feature set,we introduce the empirical mode decomposition(EMD) to decompose the original EMG signals into several intrinsic mode functions(IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines(LS-SVMs) ,the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features,the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.