最小二乘支持向量机在热舒适性PMV指标预测中的应用研究

介绍了一种新型的机器学习算法一最小二乘支持向量机的原理，并针对预测PMV指标建立了最小二乘支持向量机预测模型。该模型的预测结果表明，最小二乘支持向量机预测准确度高，计算过程速度快，可以满足以PMV指标作为被控参数的空调系统控制的要求。     

最小二乘支持向量机和小波电铲供电机组状态预测

针对电铲供电机组振动时间序列是个非线性、非平稳的复杂时间序列,难以用单一预测方法进行有效预测的问题,建立了一种基于小波分解和最小二乘支持向量机混合模型进行状态预测的方法.首先通过小波分解,将原始振动时间序列分解到不同层次,然后根据分解后各层次分量的特点选择不同的嵌入维数和LS-SVM参数分别进行预测,最后重构得到原始序列的预测值.对某电铲供电机组振动趋势的预测结果表明,该模型的预测性能好于单一的支持向量机预测方法.     

移动最小二乘支持向量机

基于移动最小二乘逐点逼近思想,移动权被引入到最小二乘支持向量机的误差变量中,得到新算法的模型.此外,证明了用移动最小二乘支持向量机作函数估计与在特征空间中用移动最小二乘法得到的解是一致的,揭示了移动最小二乘支持向量机所选择的核函数相当于移动最小二乘法所选择基函数组.数值试验与实例进一步验证所提出方法的优越性.     

基于最小二乘支持向量机的传感器动态补偿方法

介绍和比较标准支持向量机（SVM）和最小二乘支持向量机（LS-SVM）基本原理的基础上,探讨了一种利用LS-SVM进行传感器动态误差补偿的方法,并给出了相应的过程和算法。与标准SVM补偿方法比较,该方法的优点是明显的：用等式约束代替标准SVM算法中的不等式约束,将求解二次规划问题转化为直接求解线性矩阵方程,在相同样本条件下,使得补偿器构造速度提高1～2个数量级。通过对SVM和LS-SVM传感器动态补偿的仿真分析和实验结果对比表明,在噪声条件下,LS-SVM方法的补偿误差约为SVM的40％。因此,LS-SVM补偿方法学习速度快,抗噪声干扰能力强,更适合传感器动态补偿。     

基于改进果蝇算法与最小二乘支持向量机的轧制力预测算法研究

铝合金板材精轧过程中,轧制力是影响板材质量的重要因素。为了满足轧制现场的轧制力预报精度要求,采用改进果蝇算法(FOA)与最小二乘支持向量机(LSSVM)相结合进行轧制力预测。改进了果蝇算法的味道浓度判定函数和步长设定方法,采用了分组并行搜索的策略,进而提出一种基于改进FOA-LSSVM的轧制力智能预报方法。将该方法用于铝热连轧现场数据的仿真实验,结果表明样本预测误差在10%以内,其中84%的样本误差在5%以内,精度优于传统模型。     

最小二乘支持向量机在瞬变热环境实验研究中的应用

瞬变热环境下,热反应与环境参数是紧密联系的。本文基于最小二乘支持向量机LS-SVM(LeastSquares Support Vector Machine)理论,结合瞬变热环境下受试者的投票实验数据,试图将这种关系量化,以达到对瞬变热环境下整体热感觉预测的目的。通过样本测试对预测模型的验证结果表明,向冷环境过渡和向热环境过渡中误差﹤0.3的样本比例都达到了90%以上,预测结果较理想,并且预测精度优于BP神经网络所建立的模型。另外,考虑到热感觉的模糊性以及个体化差异造成的影响,还给出了测试样本集在置信水平为95%时的置信区间,能对测试样本的变化区间作出较为准确的判断。     

基于遗传算法和最小二乘支持向量机的织物剪切性能预测

提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.     

基于最小二乘支持向量机的扫描仪特征化

目的基于最小二乘支持向量机回归(LSSVR),研究扫描仪图像输入设备的特征化方法。方法以ColorChecker SG标准色卡为目标,通过最小二乘支持向量机建立RGB三通道值到CIE Lab色度值的非线性映射模型,采用基于交叉验证的网格搜索确定模型最优参数,优化LSSVR模型,实现彩色扫描仪的色度特征化。结果所建模型的训练集R-squared为0.996,验证集R-squared为0.998,训练集与验证集的CIEDE2000平均色差分别为1.1463,1.2754。结论 LSSVR模型能够较好地实现彩色扫描仪色度特征化,泛化能力较强,此模型可有效地提高彩色扫描仪特征化的精度且计算处理速度更快。     

基于威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测

为有效描述滚动轴承的退化趋势,提出结合威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测新方法。用威布尔分布形状参数作为滚动轴承的性能退化指标,将该指标作为最小二乘支持向量机的输入构造退化趋势预测模型。鉴于最小二乘支持向量机模型参数对模型的推广预测能力影响较大,选粒子群算法(PSO)优化最小二乘支持向量机模型参数,并用实测滚动轴承全寿命实验数据进行检验。结果表明该方法能获得准确的预测结果。     

基于最小二乘支持向量机时间序列瓦斯预测研究

基于统计学习预测瓦斯含量是目前瓦斯突出预测的发展方向之一。文中首先给出了最小l¨：乘支持向量机（LS—SVM）~,并在此基础上建立了预测模型,之后对时间序列上的矿井瓦斯涌出量进行相空间重构,其中嵌入维数的选取采用了微熵率法。最后通过鹤壁十矿一个突出工作面的瓦斯涌出数据对模型进行了验证。利用MATLAB7．1对其进行仿真研究。结果表明,通过训练建立的LS-SVM模型,较好地预测了这一工作面瓦斯涌出量。     

Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR

In order to eliminate the influence of unavoidable outliers in training sample on a model's performance, a novel least square support vector machine regression, which combines outlier detection approach and adaptive weight value for the training sample, is proposed and named as adaptive weighted least square support vector machine regression (AWLS-SVM). Firstly, the effective robust 3σ principle is used to detect marked outliers for the training sample. Secondly, based on the training sample without marked outliers, least square support vector machine regression is employed to develop the model and the fitting error of each sample data is obtained. Thirdly, according to the fitting error of each sample data, the initial weight is calculated. The bigger the fitting error of sample data is, the smaller the weight value of the sample data. Thus, the potential outliers, which are not detected by the robust 3σ principle and have bigger fitting errors, have smaller weight values to reduce the influence of the potential outliers on the performance of model. Then, LS-SVM is applied for the weighted sample to develop the model again. Finally, via the proposed weight value iterative method, the weight values of the training sample are converged, and the model with good predicting performance is obtained. To illustrate the performance of AWLS-SVM, simulation experiment is designed to produce the training sample with marked outlier and some non-marked outliers. AWLS-SVM, AWLS-SVM without the robust 3σ principle, LS-SVM with the robust 3σ principle, LS-SVM, and radial basis function network are applied to develop the model based on the designed sample. The results show that the influence of marked and un-marked outliers on the model's performance is eliminated by AWLS-SVM, and that the predicting performance of AWLS-SVM is the best. Furthermore, the AWLS-SVM method was applied to develop the quantitative structure–activity relationships (QSAR) model of HIV-1 protease inhibitors, and the satisfactory result was obtained.     

基于改进LSSVM的动态测量误差实时预测方法

误差修正是提高动态测量精度的有效途径,其中误差的建模是关键.在分析现有动态测量误差预测技术不足的基础上,提出基于改进的最小二乘支持向量机的动态测量误差回归建模和预测方法.在最小二乘支持向量机的基础上,通过将价值函数改为最小二乘价值函数以及用等式约束代替不等式约束,将求解的二次规划问题转变为一组等式方程,减少了待定参数的个数,很大程度地缩短了支持向量机的训练时间;同时针对最小二乘支持向量机稀疏性丢失这一缺陷,采用剪枝算法改进其性能,使其具有更好的稀疏性.通过实例验证及与其他建模方法的对比,表明该方法具有优良的预测效果和动态性能,为动态测量误差预测提供了一种新的可行方法.     

基于舍一交叉验证优化最小二乘支持向量机的故障诊断模型

提出一种基于舍一交叉验证优化最小二乘支持向量机(LS-SVM)的旋转机械故障诊断模型。首先将故障信号EMD分解为平稳IMF分量,再选择表征故障调制特征的IMF分量并构造瞬时幅值欧式范数作为故障特征矢量输入到舍一交叉验证(leave-one-outcross-validation, LOO-CV)优化线性核LS-SVM中进行故障识别。EMD分解可自适应分离故障调制信号;瞬时幅值欧式范数矢量的不同表征各类故障的差异;舍一交叉验证优化惩罚因子可以使线性核LS-SVM克服对故障类型与模式编号映射关系先验知识的依赖,提高LS-SVM的故障预测精度和自适应诊断能力。一个深沟球轴承故障诊断实例说明该模型的有效性。     

基于LSSVM的重力坝地震稳定易损性分析

提出了一种基于最小二乘支持向量机的易损性分析方法,将有限元数值计算的结果作为学习样本,建立最小二乘支持向量机模型,进而进行Monte Carlo仿真,获得结构的易损性曲线,在保证计算精度的同时,提高了计算效率。采用该方法对某重力坝挡水坝段进行了动力稳定易损性分析,并对主要参数进行了敏感性分析。结果表明,该坝段能够保证设计地震作用下的动力抗滑稳定性,且有较大的安全裕度。     

基于自适应递推LSSVM的硅锰合金成分在线预测

针对硅锰合金埋弧熔炼过程的特点,提出了一种基于自适应递推最小二乘支持向量机(ARLSSVM)的合金成分在线预测模型.该模型以实测工况参数为数据集,当新增一个样本时,分别采用增长记忆递推算法、限定记忆递推算法和缩减记忆递推算法训练最小二乘支持向量机( LSSVM),有效避免高维矩阵的求逆,加快模型更新的速度.然后通过自适...     

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.     

Probability-based least square support vector regression metamodeling technique for crashworthiness optimization problems

This paper presents a crashworthiness design optimization method based on a metamodeling technique. The crashworthiness optimization is a highly nonlinear and large scale problem, which is composed various nonlinearities, such as geometry, material and contact and needs a large number expensive evaluations. In order to obtain a robust approximation efficiently, a probability-based least square support vector regression is suggested to construct metamodels by considering structure risk minimization. Further, to save the computational cost, an intelligent sampling strategy is applied to generate sample points at the stage of design of experiment (DOE). In this paper, a cylinder, a full vehicle frontal collision is involved. The results demonstrate that the proposed metamodel-based optimization is efficient and effective in solving crashworthiness, design optimization problems.     

Sheet forming optimization based on least square support vector regression and intelligent sampling approach

In this paper, a metamodel-based optimization method by integration of support vector regression (SVR) and intelligent sampling strategy is applied to optimize sheet forming design. Compared with other popular metamodeling techniques, the SVR is based on the principle of structure risk minimization (SRM) as opposed to the principle of the empirical risk minimization in conventional regression techniques. Thus, the accuracy and robust metamodel can be obtained. The intelligent sampling strategy is a kind of design of experiment (DOE) essentially. The characteristic of this method is to generate new sample automatically by responses of objective functions. Compared with traditional DOE methods, the number of samples isn’t constant according to different cases. Furthermore, the number of samples and size of design space can be well controlled according to the intelligent strategy. To minimize both objective functions of wrinkling, crack and thickness deformation efficiently, the proposed method is employed as a fast analysis tool to surrogate the time-consuming finite-element (FE) procedure in the iterations of optimization algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is feasible for sheet forming optimization.