A least squares support vector regression coupled linear reconstruction algorithm for ECT

Linearization error of the simplified linear electrical capacitance tomography (ECT) model is one of the leading causes of ECT reconstruction errors. In this paper, the least squares support vector regression (LSSVR) is used to fit the correlation between the capacitance vector and the linearization error. And it is trained by the training samples of typical phase distributions. When removing the linearization error from equations derived by the linear model, the reconstruction problem becomes an exact linear inverse problem because the nonlinearity of ECT is completely included in the linearization error. Then a reconstruction algorithm combining the LSSVR and the Landweber iteration is proposed. Numerical results show that the proposed algorithm achieves significantly better reconstruction accuracies than the linear back projection and the Landweber algorithm for both the noise-free and noisy cases. Compared with the Landweber algorithm, The image errors of the reconstructions are reduced by about 23%–68%, and the correlation coefficient increased by about 0.04–0.14. And the calculation time of the proposed algorithm for all the tested cases is about 0.4–0.6s, which makes it have the potential for real-time imaging. Static experimental results show that the reconstructions of the proposed algorithm have more accurate phase boundary shapes and fewer artifacts.     

最小二乘支持向量机模型的简化结构研究

针对LS-SVM模型复杂度高的问题．本文提出了一种简化模型的思路。在保证LS-SVM模型性能不变的前提下,通过该模型的少量输入输出样本,利用LS-SVM建模进一步拟合该模型。使得到的新模型的复杂度降低。仿真试验表明。本文给出的模型简化方法是有效的。     

基于最小二乘支持向量机的压力传感器温度补偿

压力传感器的输出不仅随压力变化,而且易受环境温度的影响,因而限制了传感器的测量精度。为了克服压力传感器的上述缺陷,本文提出了一种基于最小二乘支持向量机的温度补偿方法,并用虚拟仪器技术予以实现。与常用的误差反传神经网络方法相比,最小二乘支持向量机可获得更好的泛化性能,不易发生局部最优及过拟合现象。因此该方法能有效地消除温度对传感器输出的影响。实例表明,补偿后的压力传感器具有更高的测量精度和温度稳定性。     

最小二乘支持向量机在空调温控系统中的应用

温度控制系统是空调控制系统的核心,传统的温控系统采用PID调节和普通模糊控制。利用最小二乘支持向量机（Least Squares SVM,LS-SVM）理论,建立模糊控制器,通过与一般模糊控制仿真实验结果进行比较,验证了其在温控系统的有效性。     

基于最小二乘支持向量机的传感器非线性动态补偿

提出了一种基于最小二乘支持向量机的非线性传感器动态测量误差的校正方法，使得通过该方法补偿的传感器具有理想的输入输出特性。先将传感器的非线性动态系统分解成线性动态子环节和非线性静态子环节串联；与之对应，非线性动态补偿过程也包含2个阶段：线性动态补偿和非线性静态校正。然后，通过函数展开将补偿器的非线性传递函数转换为等价的类线性形式一中间模型；再通过LS-SVM回归算法求取中间模型参数；最后，推导出中间模型参数与补偿器2个子模型参数之间的关系，并通过该关系实现非线性静态校正和线性动态补偿环节的同时辨识。与常规非线性动态补偿方法比较，该方法优点是明显的：（1）只需进行一次动态标定实验；（2）能给出非线性动态补偿器的数学解析表达式；（3）充分利用LS—SVM的优点，使辨识的补偿器具有更好的抗干扰能力。仿真与实际实验结果均表明该传感器非线性动态补偿方法有效。     

一种基于LS-SVM与PID复合的逆控制系统

针对逆系统中非线性逆模型辨识困难的问题,研究了基于最小二乘支持向量机(LS-SVM)的逆模型辨识及控制,并用微粒子群算法(PSO)优化LS-SVM的参数和核函数参数。提出了一种由LS-SVM的逆模型与PID结合的复合控制系统,由LS-SVM辨识非线性系统的逆模型作为前馈控制器,形成直接逆控制。同时,由PID控制器构成反馈控制,克服直接逆控制鲁棒性不强的缺陷。仿真研究结果表明LS-SVM的逆模型辨识能力强,该复合控制系统具有比基于最近邻聚类的RBF神经网络逆控制系统更优的动态跟踪性能,更好的抗干扰能力和鲁棒性。     

基于在线最小二乘支持向量机的数控机床热误差建模与补偿

为消除数控机床热误差对加工精度的影响,提出了基于在线最小二乘支持向量机的数控机床热误差建模方法。为构建机床热误差模型,进行了建模实验,采用智能温度传感器与激光位移传感器分别测量机床温度值与主轴热变形量。将获得的数据进行在线最小二乘支持向量机建模训练,构建机床热误差模型。在根据模型得出误差预测值的同时,可以不断根据在线输入的新数据修正热误差模型本身,运算时间短,适用于在线建模。实验结果表明,基于在线最小二乘支持向量机的数控机床热误差建模方法具有精度高、鲁棒性强和计算时间短的特点。在此基础上,根据在线模型进行热误差补差,可有效消除机床热误差影响,提高数控机床的加工精度。     

基于LS-SVM的油气两相流空隙率测量方法的研究

利用12电极电容层析成像系统电容传感器获取的66个电容测量值,基于最小二乘支持向量机（LS-SVM）算法,提出了两相流空隙率在线测量的新方法。该方法用LS-SVM来建立空隙率测量模型。在实际测量时,首先归一化ECT获取的电容测量值,然后将归一化电容值输入已经建立的空隙率模型即可计算出空隙率。实验结果表明：该方法是有效的,避免了复杂耗时的图像重建过程,测量误差在6%以内,测量时间小于0.08 s。     

A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine

A novel ensemble method based on principal component analysis (PCA), genetic algorithm (GA) and support vector machine (SVM) implemented in MATLAB® is presented for establishing the NO_X emissions prediction model for a diesel engine for both steady and transient operating states. The different stages of data preprocessing, modeling, optimization and prediction were discussed in detail. Normalization and PCA were used to reduce differences and redundancy of the datasets respectively. Subsequently, the SVM model was trained with 1/3 of the equi-spaced data samples (a simple DoE) selected after preprocessing. A grid search and GA were then applied as the combination strategy with the fitness function being the cross-validated root mean square error (RMSE) for optimizing the model parameters to improve the prediction accuracy. The optimal model was finally tested using the rest 2/3 data samples. Compared with other three methods, the proposed model exhibited superior accuracy both on training and testing datasets.     

基于MI-LSSVM的水泥生料细度软测量建模

针对水泥生料细度软测量模型难以建立的问题,考虑到输入变量选择易受时延的影响,提出一种基于互信息和最小二乘支持向量机(MI-LSSVM)的软测量建模方法。该方法采用互信息表征变量间的相关性,进而解决水泥生料细度软测量建模中的时延问题,并在此基础之上,提出双向选择算法获取输入变量,将得到的输入变量应用于最小二乘支持向量机中,建立水泥生料细度软测量模型,最后应用水泥厂的实际数据对基于互信息和最小二乘支持向量机的水泥生料细度软测量模型进行仿真。结果表明该方法预测精度高、泛化能力强。     

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.     

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed. Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension. Immune genetic algorithm is developed in this study to determine the optimal parameters for WSVM with the highest accuracy and generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by empirical mode decomposition (EMD). The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.     

A combined support vector machine-wavelet transform model for prediction of sediment transport in sewer

Technical design of sewer systems requires highly accurate prediction of sediment transport. In this study, the capability of the combined support vector machine-wavelet transform (SVM-Wavelet) model for the prediction of the densimetric Froude number (Fr) was compared to the single SVM and different existing sediment transport equations at the limit of deposition. The performance evaluation was performed using the R-square (R²), three relative indexes (MRE, MARE, MSRE) and three absolute indexes (ME, MAE, RMSE). The factors affecting the Fr were initially determined. After categorizing them into different dimensionless groups, six different models were found to predict the Fr. Comparisons between the obtained results showed that both the SVM and SVM-Wavelet can predict the Fr with high accuracy. However, it was found that the SVM-Wavelet (R²=0.995, MRE=0.002, MARE=0.021, MSRE=0.001, ME=0.007, MAE=0.086 and RMSE=0.114) offers higher performance than the SVM and the existing equations.     

基于SVR-GA算法的广义加工空间机床切削稳定性预测与优化研究

针对机床零件加工位置和进给方向不确定造成刀尖频响函数变化,导致切削稳定性叶瓣图与无颤振工艺参数预测具有不确定性问题,提出一种耦合支持向量回归机(SVR)与遗传算法(GA)的切削稳定性预测与优化方法。该方法采用锤击法模态实验和空间坐标变换,获取样本空间不同加工位置与进给方向的刀尖频响函数;进而结合传统切削稳定性预测方法构建以各向运动部件位移、进给角度、主轴转速、切削宽度、每齿进给量为输入的极限切削深度SVR预测模型;采用该SVR模型作为切削稳定性约束建立材料切除率优化模型,通过遗传算法求解各运动轴位移、进给角度与切削参数的最优配置。以某型加工中心展开实例研究,实验结果表明获取的优化配置能实现稳定切削,验证了该方法的有效性。