Generalized Predictive Control with Online Least Squares Support Vector Machines

这份报纸基于能有效地处理非线性的系统的联机最少的广场支持向量机器(LS-SVM ) 建议一个实际概括预兆的控制(GPC ) 算法。在每个采样时期，算法递归地由增加新数据对并且在实时性质上从考虑删除最不重要的修改模型。删除的数据对被 lagrange 的绝对值从最后一个采样时期更多样地决定。当增加新数据对并且删除存在的时，纸给模型参数的递归的算法分别地，一个大矩阵的倒置被避免，存储器能被算法完全控制。非线性的 LS-SVM 模型在每个采样时期在 GPC 算法被使用。抵销过程的 pH 上的概括预兆的控制的实验显示出建议算法的有效性和实物。

ONLINE PARSIMONIOUS LEAST SQUARES SUPPORT VECTOR REGRESSION AND ITS APPLICATION

A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector regression （LS-SVR）, and the approach is called the OPLS-SVR for short. Hence, the response time is curtailed. Besides, an OPLS-SVR based analytical redundancy technique is presented to cope with the sensor failure and drift problems to guarantee that the provided signals for the aeroengine controller are correct and acceptable. Experiments on the sensor failure and drift show the effectiveness and the validity of the proposed analytical redundancy.

Nonlinear correction of photoelectric displacement sensor based on least square support vector machine

A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.

Endpoint Prediction of EAF Based on Multiple Support Vector Machines

The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation

Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hysteresis loop that exists in the actual maneuvering process of the aircraft.Here,an excitation input suitable for nonlinear system identification is introduced to model unsteady aerodynamic forces with any motion in the amplitude and frequency ranges based on the Least Squares Support Vector Machines(LS-SVMs).In the selection of the input form,avoiding the use of reduced frequency as a parameter makes the model more universal.After model training is completed,the method is applied to predict the lift coefficient,drag coefficient and pitching moment coefficient of the RAE2822 airfoil,in sine and sweep motions under the conditions of plunging and pitching at Mach number 0.8.The predicted results of the initial unstable process and the final stable process are in close agreement with the Computational Fluid Dynamics(CFD)data,demonstrating the feasibility of the model for nonlinear unsteady aerodynamics modeling and the effectiveness of the input design approach.

SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.

Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm

Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.

基于沙地猫群优化–最小二乘支持向量机的动态NOx排放预测

针对火电机组频繁调峰导致机组燃烧状态不稳,进而导致锅炉出口NOx浓度波动范围大的问题,提出一种基于沙地猫群优化(sand cat sarm optimization,SCSO)的最小二乘支持向量机(leastsquaressupportvectormachine,LSSVM) NOx动态预测模型。首先利用k近邻互信息计算时间延迟的同时筛选辅助变量。然后,基于SCSO算法进行输入变量阶次的选择。使用包含辅助变量时间延迟和阶次的信息作为模型的输入,SCSO算法优化最小二乘支持向量机参数,建立动态NOx排放最小二乘支持向量机预测模型(SCSO-LSSVM动态软测量模型)。最后将模型与未加入迟延的LSSVM模型,加入迟延的LSSVM模型和粒子群优化算法(particle swarm optimization,PSO)优化最小二乘支持向量机参数的动态软测量模型进行对比验证。结果表明,相较于其他模型,该文建立SCSO-LSSVM动态软测量模型均方根误差、平均绝对误差、平均绝对误差最小,预测精度最高,而且在NOx浓度剧烈波动时也能够较好地预测NOx浓度,具有很好的动态特性。

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

基于麻雀算法优化支持向量机的NOx浓度预测

煤炭作为火电厂发电的主要能源,其在锅炉内焚烧过程中会产生大量的氮氧化物。各电厂一般利用烟气自动监控系统对其浓度进行实时测量,但由于测量时存在较大迟延,不能准确地反映SCR系统NOx浓度的实时变化。因此提出了一种基于改进麻雀算法优化最小二乘支持向量机的NOX浓度预测方法。首先,引入余弦因子改进麻雀算法中的比例算子,将迭代次数信息引入到迭代过程中,平衡算法前后期的全局与局部搜索能力。其次,使用新的变异算子代替原算子,将混沌理论融合到麻雀算法,解决了算法全局搜索能力较差、初始化麻雀分布不稳定及发现者位置更新方式不足的问题。最后,采用改进麻雀算法(CDE-SSA)对最小二乘支持向量机(LSSVM)进行参数寻优。实验结果证明,方法在NOX浓度预测的精度和稳定性上均表现出了良好的性能。

Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines

This paper deals with Wiener model based predictive control of a pH neutralization process.The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM).Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification.Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models,Laguerre-polynomial Wiener models,and linear Laguerre models.The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process.The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC,Laguerre-polynomial Wiener model based NMPC,and linear model predictive control (LMPC).Validation results show that the proposed NMPC outperforms the other three controllers.

基于近红外光谱的烤烟油分识别研究

【目的】对烤烟油分等级进行科学预测,实现不同油分档次烤烟的快速光谱鉴别。【方法】对代表性植烟县的299份全叶位覆盖的不同油分档次云烟87烟叶样本进行近红外光谱采集,利用一阶导数(D1)、归一化(NOR)、小波变换(WAVE)、标准正态化(SNV)和多元散射校正(MSC)共5种方法对光谱数据预处理后,考察了线性的偏最小二乘判别分析(PLS-DA)和非线性的最小二乘支持向量机(LS-SVM)判别模型的判别效果。【结果】对近红外原始光谱数据进行主成分降维后,所构建的PLS-DA油分档次分类模型训练集的准确率可达100.0%,但测试集仅有79.8%,经过D1、NOR、SNV和MSC预处理后,模型的测试集准确率分别提高到了85.9%、90.0%、83.8%和83.8%;基于对近红外原始光谱数据直接构建的LS-SVM油分档次分类模型的训练集准确率也达100.0%,测试集达到92.9%,经过NOR、WAVE、SNV和MSC预处理后测试集的准确率均提高到了95.0%以上,以MSC预处理的99.0%的准确率最高。【结论】多元散射校正预处理结合LS-SVM法构建的油分档次判别模型效果最好,提高了烤烟油分判定效率。

基于水资源空间均衡的“四水四定”调控模型构建

为实现未来不同时间尺度下的水资源空间均衡与动态调控,创建了一套完整严谨、可动态调控的“四水四定”模型体系。通过模糊信息粒化窗口的支持向量机模型预测区域未来总用水量,利用基于时间序列相似性分析的自回归支持向量机模型预测区域未来分用水量,并对两类数据进行不确定性分析;构建了复杂回归函数对各类用水指标进行情景预测,经统计检验后将其作为当前用水模式下未来用水指标;构建了“四水四定”水资源承载力模型和水资源空间均衡模型,基于未来总用水量、未来各分用水量、未来用水指标,选用水资源负载系数、用水效益和水土资源匹配系数3个指标,结合基尼系数量化水资源空间均衡度,分析当前用水模式下未来水资源均衡度;构建了最优化模型,以最小化基尼系数为目标函数调整未来用水模式,实现水资源动态调控。所创建的模型体系可以实现未来不同时间尺度下的水资源空间均衡与动态调控。

基于改进SVM的电力工程造价预测

针对支持向量机求解速度较慢且用于预测电力工程造价的性能不理想等问题,提出了一种基于改进SVM的电力工程造价预测模型。该模型全面考虑了电力工程成本的组成要素并进行参数归一化处理,利用最小二乘估计改进SVM模型,同时采用遗传算法求解LSSVM的参数最优值,并通过优化后的GA-LSSVM模型实现对电力工程成本的预测。基于MATLAB仿真平台的仿真实验结果表明,模型预测的工程成本与实际值较为接近,归一化均方误差与平均绝对百分比误差分别为18.34万元和3.58%,且预测时间仅为256 ms,证明了其整体性能优于其他对比模型。

地面气象测温传感器设计及实验研究

地面气象测温传感器受太阳辐射的影响,测量时其辐射误差可达1 K量级。针对此问题,设计了一种新型地面测温传感器结构。使用计算流体动力学(computational fluid dynamics,CFD)方法将多个气象参数下的仪器辐射误差进行量化,采用粒子群优化支持向量机算法对其结果进行拟合,得到辐射误差修正方程。以076B强制通风仪器的测量值作为温度基准开展场外实验。结果表明,设计的地面气象测温传感器经修正方程修正后的测量结果与温度基准的均方根误差(root mean square error,RMSE)和平均绝对误差(mean absolute error,MAE)分别为0.029 K和0.027 K,可将辐射误差保持在0.05 K以内。

基于数据模型协作的海上钻井溢流早期预测预警

为防止海上钻井过程中井喷事故的发生,提出基于数据模型协作的海上钻井溢流早期预测预警方法。首先,建立基于粒子群优化(PSO)-最小二乘支持向量机(LSSVM)(PSO-LSSVM)的溢流风险预测模型,预测钻井监测参数未来时长内的趋势,并分析溢流事件与表征参数之间的关联关系;然后,建立基于朴素贝叶斯方法的钻井单参数溢流概率估算模型,并通过优化的D-S方法融合多个钻井参数的概率,分级预警溢流事件。结果表明:基于PSO-LSSVM的预测模型所得的溢流表征参数,预测误差较低;因对溢流事件的敏感度不同,单钻井参数所表征的溢流事件概率存在一定偏差;融合后的预警模型能够解决单参数的预警时间不一致的问题,排除误报警的可能。

机载下投式探空温度传感器设计与实验研究

为降低太阳辐射对机载下投式探空仪中温度传感器的影响,设计了一种阵列式NTC珠状热敏电阻探空温度传感器。该传感器阵列通过探头间的太阳辐射误差比值推算大气环境温度的真实值。首先,通过计算流体动力学(computational fluid dynamics, CFD)方法选出传感器探头的最优引线夹角,再对表面涂覆不同太阳辐射反射率涂层的探头进行太阳辐射误差比值计算;然后利用支持向量机(support vector machine, SVM)拟合仿真,得到辐射误差比值拟合模型;最后,搭建低气压风洞以及太阳模拟器实验平台。实验结果表明,该温度传感器阵列可将测量误差降低至0.065℃,均方根误差降至0.078℃,有望将太阳辐射误差对下投式探空仪温度测量的影响降低至0.1℃以内。

基于高光谱技术的场地土壤重金属污染快速调查研究

为了准确预测场地土壤重金属分布状况,实现土壤重金属污染快速调查,以某废弃助剂厂填埋区土壤为研究对象,基于高光谱数据,利用单变量回归模型、偏最小二乘回归模型和支持向量机模型估算土壤重金属Cr、Ni、Cu、Zn、Cd、Pb、As和Hg的含量。结果表明,土壤光谱反射率与各重金属含量均呈负相关;偏最小二乘回归模型和支持向量机模型对8种重金属的预测精度均优于单变量回归模型,偏最小二乘回归模型为Cd、Pb、Cr和Ni的最佳估算模型,支持向量机模型为Cu、As、Zn和Hg的最佳预测模型;研究区土壤重金属反演结果在趋势上与实验室分析结果基本一致,高值区和极值点分布亦较为吻合,能够圈定存在重金属污染风险的区域,同时提供技术支撑,实现场地土壤重金属污染的快速调查。

基于鲸鱼优化算法-支持向量回归的汽车运动状态估计

为了不依赖动力学模型精度而准确地获取车辆运动状态信息,提出一种基于鲸鱼优化算法-支持向量回归(WOA-SVR)的车辆状态估计算法。首先通过分析车辆动力学基本特性,设计了侧向速度、横摆角速度与车速分离的支持向量回归估计架构;然后对支持向量回归(SVR)模型进行多种行驶工况组成的数据集训练,在训练过程中运用鲸鱼优化算法对松弛变量中的惩罚因子c与核函数参数g进行寻优;最后对估计算法进行单移线、扫频试验虚拟仿真和实车ABS制动、双移线试验验证。结果表明,该算法有效提高了估计精度,且对车速的变化具有鲁棒性,可以实现准确的不依赖动力学模型精度的汽车运动状态估计。

基于最小二乘支持向量机的电网企业供应链碳排放预测方法研究

电网企业供应链碳排放的预测对推动产业链供应链绿色转型具有重要意义,为此提出基于最小二乘支持向量机的电网企业供应链碳排放预测方法。首先,利用4E平衡模型获取电网企业供应链碳排放数据;其次,利用PLS-VIP算法对碳排放数据实施数据筛选,得到有效的碳排放数据变量;最后,引入最小二乘支持向量机,构建碳排放预测模型,并且采用量子粒子群优化算法对其展开优化,实现电网企业供应链碳排放高精度预测。实验结果表明,所提方法在保证预测过程较高稳定性的同时,一定程度上提高了预测精度和预测效率。