基于LS-SVM的复轮廓波变换的图像去噪

针对传统的轮廓波变换图像去噪时引入边缘混叠现象,提出了复轮廓波变换(Complex Contourlet Transform,CCT)和最小二乘支持向量机(LS-SVM)的图像去噪方法.该方法充分利用了复轮廓变换的平移不变性、多方向性以及LS-SVM的小样本学习能力,应用训练好的LS-SVM模型将含噪图像的CCT系数分为含噪点和非含噪点,进行去噪处理.仿真结果表明该算法有效保护图像边缘纹理信息,其峰值信噪比明显高于其他算法,并且具有良好的视觉效果.     

Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification

In this paper, a new methodology for identifying multiple inputs multiple outputs Hammerstein systems is presented. The proposed method aims at incorporating the impulse response of the system into a least-squares support vector machine (LS-SVM) formulation and therefore the regularisation capabilities of LS-SVM are applied to the system as a whole. One of the main advantages of this method comes from the fact that it is flexible concerning the class of problems it can model and that no previous knowledge about the underlying non-linearities is required except for very mild assumptions. Also, it naturally adapts to handle different numbers of inputs/outputs and performs well in the presence of white Gaussian noise. Finally, the method incorporates information about the structure of the system but still the solution of the model follows from a linear system of equations. The performance of the proposed methodology is shown through three simulation examples and compared with other methods in the literature.     

基于短时LS-SVM的GNSS铁路路基沉降监测数据分析研究

在GNSS(全球导航卫星系统)铁路路基沉降监测中,卫星信号受到周围环境的影响,同时实时监测中数据传输的不可靠性,导致监测结果往往有很强的不稳定性,有效地剔除粗差能提高GNSS监测的可靠性.鉴于最小二乘支持向量机有很强的非线性运算能力与数据拟合能力,建立短时最小二乘支持向量机铁路沉降数据预测模型,在监测中融合该模型,提高监测的有效性.分别运算该模型对山西中南部铁路通道重载综合试验地的3个监测点进行了分析处理,结果表明融合短时最小二乘支持向量机的预测模型有效抑制了GNSS监测中的离散误差,提高了GNSS监测系统的可靠性与稳定性,为铁路路基沉降监测提供了更为有效的手段.     

一种基于全局代表点的快速最小二乘支持向量机稀疏化算法

非稀疏性是最小二乘支持向量机（Least squares support vector machine,LS-SVM）的主要不足,因此稀疏化是LS-SVM研究的重要内容.在目前LS-SVM稀疏化研究中,多数算法采用的是基于迭代选择的稀疏化策略,但是时间复杂度和稀疏化效果还不够理想.为了进一步改进LS-SVM稀疏化方法的性能,文中提出了一种基于全局代表点选择的快速LS-SVM稀疏化算法（Global-representation-based sparse least squares support vector machine,GRS-LSSVM）.在综合考虑数据局部密度和全局离散度的基础上,给出了数据全局代表性指标来评估每个数据的全局代表性.利用该指标,在全部数据中,一次性地选择出其中最具有全局代表性的数据并构成稀疏化后的支持向量集,然后在此基础上求解决策超平面,是该算法的基本思路.该算法对LS-SVM的非迭代稀疏化研究进行了有益的探索.通过与传统的迭代稀疏化方法进行比较,实验表明GRS-LSSVM具有稀疏度高、稳定性好、计算复杂度低的优点.     

基于最小二乘支持向量机建模的电液伺服系统故障检测方法

基于最小二乘支持向量机建模的方法,研究了电液伺服系统的故障检测问题.介绍了基于最小二乘支持向量机进行建模的基本原理,分析了电液伺服系统所存在的非线性和故障模式,给出了基于最小二乘支持向量机建模进行故障检测的方法,试验结果表明,由支持向量机模型预测输出与实际输出相比较所形成的残差,能够准确地反映故障发生与否的情况;同时,与神经网络方法和普通的支持向量机方法相比,最小二乘支持向量机方法更适合工程应用,效果更好.     

主成分分析和最小二乘支持向量机模型在铁水硫和硅含量预测中的应用

良好的铁水质量是铸铁性能可靠性和稳定性的保证,而铁水中硫(S)含量和硅(Si)含量是衡量铁水质量的主要指标,因此在出铁前精准获取铁水S含量和Si含量具有非常重要的意义。实验提出一种结合主成分分析(PCA)和最小二乘支持向量机(LS-SVM)模型的铁水S含量和Si含量的预测方法。将某钢厂大型高炉的在线采集数据作为研究对象,首先对影响铁水中S含量和Si含量变化因素的数据做主成分分析,求取主成分作为模型的输入变量,其次建立最小二乘支持向量机预测模型对铁水S含量和Si含量进行预测。在S含量预测过程中,正则化参数gam和核函数参数sig分别取20、700时,预测误差最小,其均方根误差为0.0012,仿真时间为0.423105s;Si含量预测过程中正则化参数gam和核函数参数sig分别取40、500时预测误差最小,均方根误差为0.0238,仿真时间为0.079522s。最后将实验结果与传统最小二乘支持向量机(LS-SVM)和结合PCA的BP神经网络预测模型(PCA+BP神经网络)的结果对比,后两组对比实验关于S含量预测的均方根误差分别为0.0015和0.0014,仿真时间分别为1.320842s和2.245967s;后两种对比实验关于Si含量预测的均方根误差分别为0.0316和0.0325,仿真时间分别为0.459671s和2.061576s。实验结果表明,实验方法更加全面地考虑了所有因素对铁水中S含量和Si含量变化的影响,具有训练时间短、预测精度高等优点。     

Modeling of machined depth in laser surface texturing of medical needles

The present paper addresses the experimental modeling of process parameters in laser surface texturing (LST) of medical needles. First, experiments were carried out based on Taguchi methodology. The laser process parameters considered during LST were the circumferential overlap, axial overlap and the overscan number. A second-order regression model of the machined depth for LST was developed based on the experimental results. Second, a predictive model for the machined depth based on least squares support vector machines (LS-SVM) with radial basis functions was constructed using the same experimental swatches. Grid search and leave-one-out cross-validation were used to determine the optimal parameters of the LS-SVM model. The comparison between the second-order regression model and the LS-SVM model was carried out. The experiments indicated that the LS-SVM model is capable of better predictions of the machined depth than the second-order regression model. The validity of the LS-SVM model has been checked through the creation of micro-channels with blended edges. It was found that the predicted profile was in a good agreement with the experimental profiles. The LS-SVM model can be used to predict machined geometry of the micro-channels on medical needles.     

基于支持向量机的天然气管道小泄漏信号检测

针对天然气管道小泄漏信号的识别问题,提出了一种基于支持向量机的回归算法模型。首先根据香农采样定理和SCADA系统的采样频率,建立了小泄漏工况的故障样本库。然后,利用小泄漏工况下的压力波数据作为训练样本,建立了LS-SVM预测模型。最后,对小泄漏工况、正常工况和调阀工况下的压力波数据进行了信号检测。结果表明,3种工况的一步预测误差的均值分别为9.8631×10-4、0.7886和2.7400×10-2,不在同一个数量级。因此,如合理设定门限值,LS-SVM检测器就能对管道小泄漏信号实现有效的识别。     

Identification of MIMO Hammerstein models using least squares support vector machines

This paper studies a method for the identification of Hammerstein models based on least squares support vector machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. This is done by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples. Another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness.     

基于在线LS-SVM的动力电池SOC估算方法研究

荷电状态(SOC)是描述动力电池状态的重要参数之一,提高SOC估计的准确性对电动汽车电池管理系统的研究至关重要。提出一种改进的最小二乘支持向量机(LS-SVM),动态地调整模型参数,对电池的开路电压(OCV)进行在线实时估计;通过SOC与OCV的关系确定初值,采用安时积分法估算SOC;并利用OCV的偏差信息对电池SOC进行修正,有效地补偿拟合误差和安时积分法产生的累计误差。仿真实验结果表明,在线LS-SVM算法能准确地逼近实际SOC值,平均绝对误差为1.279 3%。