一种改善国产热偶规管对热稳定性的方法

由该方法制造的带自补偿热偶真空规管已申请中国专利 国产热偶规管是我国目前应用最普遍的粗低真空测控传感器。国产热偶规管的对热稳定性与美国ＤＶ系列热堆规管相比存在着明显差距，其原因就在于规管中的热端接点和冷端接点的热学特性不一致。国产规管中冷端接点的热惯性比热端接点大得多，从而使冷端接点的温度响应比热接点慢得多。本文提出的改进方法能将国产规管冷端的热惯性降低到与热端接点相当的程度。采用本方法不仅能提高国产热偶规管的热稳定性，而且还不需要对原来所配的真空计作任何改动，故极易推广普及。 并在文末附录部分对“环境热辐射与安装方位对热偶规的影响”一文（《真空１９９０（１）５１～５４）的理论推导和结论提出质疑。     

基于SVR构造FLANN的传感器动态补偿研究

提出了一种利用支持向量回归机(SVR)对函数链接型神经网络(FLANN)进行构造的新方法,并将其应用于传感器动态补偿.文中将SVR的解与常规FLANN估计进行对比,发现两者具有相同的问题形式,因此,在适当的参数条件下可通过SVR对FLANN进行优化构造.与常规FLANN构造方法比较,SVR-FLANN具有明显特点,即将权值迭代逼近问题转化为二次规划问题求解,使得在整个训练过程中有且仅有一个全局极值点,确定了所构造FLANN补偿器的唯一性.实际压力传感器动态补偿实验结果表明:用该方法构造的补偿器与常规方法相比,具有更高的精度、更强的抗干扰能力及更稳定的补偿效果.因此,更适合传感器动态补偿.     

一种新型回归支持向量机的学习算法

支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种具有很好泛化性能的回归方法,本文对标准支持向量机稍作改动,提出了一种新型回归支持向量机,并推导出它的对偶表达方式,随后利用一个优化定理设计了一个多变量更新学习算法,该算法能单调收敛于极值点,并具有简单的迭代方式,仿真实例说明所提出的回归支持向量机及其训练算法具有较好的学习性能.     

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。     

基于多输出支持向量回归机的有限元模型修正

为了克服神经网络以及单输出支持向量回归算法在有限元模型修正中的不足,提出了基于多输出支持向量回归算法的有限元模型修正方法。根据5-折交叉验证法选择支持向量回归机的参数,用均匀试验设计法构造样本,联合结构的动力和静力响应数据作为输入,多个设计参数作为输出,以支持向量回归机逼近输入输出二者之间的非线性映射关系,然后利用支持向量回归机的泛化推广能力,求解设计参数的目标值。空间网格结构数值模型的分析结果表明,该方法能同时修正多个设计参数,在少量样本的情况下具有较高的修正精度,为有限元模型修正提供了一种新的探索。     

多功能真空校准装置

为满足航天器热试验常用真空计及标准漏孔的校准需要,研制了多功能真空校准装置。该装置可用于进行热偶真空计、压阻规、电容薄膜真空规、潘宁规、热阴极电离规等真空测量传感器的校准,同时也可以用于渗透型真空漏孔的校准。装置选用静态比对法、动态比对法、质谱比对法设计建成,真空校准范围为1.33×10~5Pa~1×10~(-4)Pa,真空漏孔校准范围为5×10~(-5)~5×10~(-9)Pa·m~3/s,装置智能化水平高,操作简便,适合真空规管的批量校准。     

基于SVR-OCSVM模型的多联机系统用能评估与诊断

直接根据多联机系统能耗数据的变化来判断导致能耗大幅波动的因素是很困难的。本文提出一种有效的可用于多联机系统的能耗评估与诊断方法:将支持向量回归(SVR)算法与单类支持向量机(OCSVM)算法相结合,首先通过提取系统能耗数据集特征,去除非稳态数据,根据提取的特征变量与系统能耗建立SVR模型,预测多联机系统能耗;然后将实际能耗值与预测能耗值之差和之比分别标准化,作为输入变量,建立单类支持向量机(OCSVM)模型进行样本判别,确定是否为导致系统能耗异常的原因,以此评估诊断多联机系统能耗情况。本文基于多联机能耗正常的数据集构建了能耗评估与诊断模型,并用多联机系统能耗异常数据集验证了模型的可靠性。结果表明:基于SVR-OCSVM模型的能耗评估与诊断模型具有较高的准确度,基本能达到70%以上。     

基于支持向量机的传感器非线性动态补偿方法

提出了应用支持向量机(LS-SVM)实现传感器非线性动态补偿方法.LS-SVM的训练过程遵循的是结构风险最小化原则,而不是通常神经网络的经验误差最小化,可获得更好的泛化性能,不易发生局部最优及过拟合现象,因此可弥补应用人工神经网络进行传感器非线性动态补偿的缺陷.通过实例验证了该方法的可行性,结果表明,即使当传感器动态模型存在严重非线性,且有测量噪声存在,该方法也仍然有效.     

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GM-噪声SVR方法的矿山设备可靠性参数估计

为了有效估计小子样条件下矿山设备的三参数威布尔分布可靠性模型参数,提出基于GM-噪声SVR的参数估计方法。该方法以灰色估计法(GM)为基础估计模型的位置参数,采用基于训练样本数量和噪声参数寻优的ε - 带支持向量回归机(ε-SVR)估计尺度参数和形状参数,并通过拟合的三参数威布尔分布函数分析预测和解决设备的可靠性问题。算例结果表明,GM-噪声SVR方法可以很好地用于矿山设备可靠性模型参数估计,估计某带式输送机三参数威布尔分布可靠性模型的位置参数、尺度参数和形状参数依次为3.1525、188.3763、1.0476,平均无故障时间为188 h,标准均方根误差NRMSE为0.0519。这表明该方法的可行性和有效性。     

Modeling of Non-uniformly Sampled Systems by Support Vector Regression with Applications

This paper presents a modeling method for a non-uniformly sampled system based on support vector regression (SVR).First,a lifted discrete-time state-space model for a non-uniformly sampled system is derived by using the lifting technique to reduce the modeling difficulty caused by multirate sampling.Then,the system is divided into several parallel subsystems and their input-output model is presented to satisfy the SVR model.Finally,an on-line SVR technique is utilized to establish the models of all subsyste...     

一种光滑支持向量回归机新函数的研究

为支持向量回归机提供了一个新的光滑函数,即运用三次样条函数和复合函数的方法,得到一种新的光滑支持向量回归机——三次样条光滑支持向量回归机(TSSSVR).对该支持向量回归机的光滑函数进行了逼近性能和收敛性的分析,并说明该光滑函数比以往的光滑函数具有更高的逼近精度和收敛速度.     

Hybrid approach for remaining useful life prediction of ball bearings

Remaining useful life (RUL) prediction plays an important role in predictive maintenance systems to support decision‐makers for arranging maintenance tasks and related resources. We propose a hybrid approach that is combined an exponential weighted moving average (EWMA) control chart for anomaly detection and machine learning models such as support vector regression (SVR) and random forest regression (RFR) with differential evolution (DE) algorithm to predict the RULs of ball bearings. Here, DE algorithm is used to find the optimal hyperparameters of SVR model. The datasets of ball bearings from the Prognostics Data Repository of NASA are used to compare the prediction performance of different methods. The degradation behavior of training data from the anomaly time to the end of life is used to transfer learning for the testing data in the SVR and RFR models. The results indicate that the proposed methods outperform the other four existing methods in terms of score. Therefore, the proposed hybrid approach is a reliable tool for the RUL prediction of ball bearings.     

Forecasting systems reliability based on support vector regression with genetic algorithms

This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error.     

Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR

A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid.     

基于粒子群支持向量回归的原位自生工艺参数优化

综合应用激光熔覆和原位反应增强金属基复合材料,是当前金属基复合材料研究领域的一个热点,本文采用该工艺制备铁基表面复合材料,重点考虑该工艺参数的确定问题.根据在不同工艺参数下合成的铁基表面的WC体积分数实测数据集,提出建立不同工艺参数下WC体积分数的支持向量回归预测模型,并与基于人工神经网络模型(ANN)的预测结果进行比较.结果显示:对于相同的训练样本和检验样本,SVR预测模型比ANN预测模型具有更强的泛化能力.最后根据建立的预测模型,应用粒子群算法寻优得到最优工艺参数,该工艺参数在实际实验过程中的应用,验证了该方法的有效性.     

Autonomic point cloud-based surface reconstruction using SVR

A surface reconstruction framework based on support vector regression (SVR) to generate a three-dimensional (3D) model is proposed in this paper. It can reduce the noise in sampled data as well as repair the holes by handling the missing data during the acquisition phase. SVR is quite efficient for surface reconstruction using parameter tuning and selective data sampling. Automatic parameter tuning of SVR is proposed using two techniques: particle swarm optimization (PSO) and genetic algorithm (GA). Independent component analysis (ICA) is a feature-preserved non-uniform simplification method which is applied to simplify point set by optimal attribute selection. First, under-sample the data, remove the redundancy, reduce the features using ICA and construct the surface using SVR. Both theoretical analysis and experimental results show that the performance of the proposed method yields an average SVR error ≈ 3% on the publicly available datasets. For majority of standard datasets, PSO–SVR is found superior to GA–SVR in convergence speed. Details of the surface are also preserved well which makes it suitable for 3D surface reconstruction.     

Kernel principal component analysis and support vector machines for stock price prediction

Technical indicators are used with two heuristic models, kernel principal component analysis and factor analysis in order to identify the most influential inputs for a forecasting model. Multilayer perceptron (MLP) networks and support vector regression (SVR) are used with different inputs. We assume that the future value of a stock price/return depends on the financial indicators although there is no parametric model to explain this relationship, which comes from the technical analysis. Comparison studies show that SVR and MLP networks require different inputs. Furthermore, proposed heuristic models produce better results than the studied data mining methods. In addition to this, we can say that there is no difference between MLP networks and SVR techniques when we compare their mean square error values.     

基于相空间重构和遗传优化SVR的机械设备状态趋势预测

针对机械设备振动信号序列的非线性、非平稳性特点,提出了一种基于相空间重构与遗传优化支持向量回归机的设备状态趋势预测方法。首先,采用相空间重构技术将一维振动信号时间序列转化成矩阵形式,自适应地选取特征,以相点作为输入特征训练SVR预测器;然后应用自适应遗传算法对惩罚因子、不敏感系数以及高斯核宽度进行同步优化,自动获取最佳的建模参数;最后构建SVR预测模型,并将其应用于某机组振动信号预测。实验结果表明,无论是单步还是24步预测,本文所提遗传优化SVR模型的预测精度都要比标准SVR模型的预测精度高,说明该方法对机械设备的运行状态趋势具有较好的预测能力。