全相关核偏最小二乘故障诊断方法及在抽油机上应用

针对油田抽油机生产数据存在强非线性和强耦合性, 导致故障诊断困难的问题, 本文提出一种全相关动态 核偏最小二乘(FCDKPLS)故障诊断方法. 首先, 构建抽油机生产数据自回归模型, 反映数据变量间的动态特性; 其 次, 分析了KPLS算法中输出变量与输入变量残差子空间的相关性, 为此, 在输出模型上构建一个辅助矩阵, 从而表 征输入变量与输出变量的全相关性, 建立输入变量和输出变量之间更直接的联系. 最后, 将提出的全相关动态偏最 小二乘方法应用于抽油机过程故障诊断, 实验结果表明本文提出方法的有效性.     

基于Logistic回归的社区满意度模型

在论述顾客满意度模型是具有二分类因变量的非线性模型的基础上，提出一种基于Logistic回归的联立方程模型．首先采用因子分析法提取影响满意度和忠诚度的潜在变量；然后对它们满意与否、忠诚与否进行Logistic回归，建立一个递归联立方程模型；最后结合社区满意度实例进行了实证研究．     

马尔可夫协整回归模型的动态滤波估计

本文提出动态滤波估计方法估计马尔可夫协整回归模型的参数.利用领先和滞后方法构造辅助的动态回归模型,以消除解释变量和误差序列间的相关性以及误差自相关性对估计结果的影响.在Hamilton滤波基础上,应用极大似然方法估计辅助模型的参数.模拟计算结果表明动态滤波估计方法能降低误差序列相关性造成的估计偏差.对1990年1月至2011年10月的中国进出口贸易数据,利用所提方法建立了马尔可夫协整回归模型.     

带隐变量的回归模型EM算法

带有隐变量的回归模型具有非常广泛的应用场合,隐回归模型的参数求解问题依赖于自变量的分布假设。基于自变量的beta分布的假设条件,给出了隐回归模型的EM算法,详细地推导了模型中的参数求解过程,给出了使用牛顿法求解beta分布参数的算法,并提出一个合适的初值选择算法。在模拟数据和真实数据的基础上进行了详细的比较性试验,结果表明,对具有不同分布特征的因变量观察值,EM算法能够有效地求解隐回归模型的参数。     

基于MCP惩罚的群组变量选择和AdaBoost集成剪枝

针对高维群组变量下的分类问题,本文提出了一种基于MCP惩罚的AdaBoost集成剪枝逻辑回归模型(AdaMCPLR),将MCP函数同时应用于特征选择和集成剪枝,在简化模型的同时有效地提升了预测精度.由于传统的坐标下降算法效率较低,本文引用并改进了PICASSO算法使其能够应用于群组变量选择,大大提高了模型的求解效率.通过模拟实验,发现AdaMCPLR方法的变量选择和分类预测效果均优于其他预测方法.最后,本文将提出的AdaMCPLR方法应用于我国上市公司财务困境预测中.     

基于样条变换偏最小二乘回归应用航油干点的软测量

在偏最小二乘回归和样条变换理论研究的基础上,提出炼油装置常压塔航油干点的软测量.采用偏最小二乘同归方法筛选一种辅助变昔和建立航油干点的软测最模型.仿真结果表明,本方法选择的辅助变量携带信息量大,对主导变量解释能力强.如样本集相同.比RBF网络和支持向量机软测量模型预测精度高,泛化能力强.     

基于PCA-RFR的传感器故障定位方法

基于数据驱动的故障诊断方法近些年来得到广泛的研究和应用,但这些方法主要针对于故障检测,对于故障根源的定位尚未得到充分解决。本文提出一种基于主成分分析(PCA)和随机森林回归(PFR)的因果分析故障定位方法(PCA-PFR)。该方法通过将离线故障数据段中的变量作为输入,与之对应的统计量作为输出建立随机森林回归模型,然后通过模型的变量重要性度量来得到过程变量对统计量的因果关系系数,其中值越大的变量被认为越有可能是引起故障发生的故障变量。最后通过一个数值案例和TE过程仿真实验,表明该方法的有效性。     

基于Granger因果关系的多变量时间序列预测模型

时间序列数据包含内在的时序结构,而传统的针对多变量时间序列的预测方法没有考虑变量序列的历史观察值的影响。为此,提出一种基于Granger因果关系挖掘的多变量时间序列预测模型。通过选择有效的因变量并加入其滞后观测期来提高支持向量回归对目标序列的预测,同时也提供了较好的因果解释性。理论推导和实验结果表明,该方法不仅能获得比传统方法更精确的预测效果,而且减少了参与运算的变量时间序列。     

基于隐回归的用户关系强度模型

为了科学合理地度量社会网络中用户间的有向关系强度,基于用户有向交互次,提出一个度量用户交互强度的光滑模型。将用户关系强度作为隐变量,交互强度作为因变量,构建度量用户关系强度的隐变量回归模型,并给出求解隐变量回归模型的最大期望(EM)算法。分别从人人网和新浪微博采集了数据集,从最佳好友、强度排序等方面进行了大量实验。在人人网实验中,通过关系模型选择的TOP-10好友与人工标注结果比较,得出NDCG均值为69.48%,平均查准率均值(MAP)为66.3%,与对比算法相比有明显提高;在大规模新浪微博数据集实验中,将关系强度大的节点作为传染模型的源节点的传播范围相较于选择其他节点作为源节点平均提高了80%。实验结果说明所提模型能够有效度量用户间的关系强度。     

基于逐步条件相关性分析的因果关系发现方法

从数据中发现与一个变量有直接因果关系的其它变量是一种非常有价值的技术.本文针对回归分析中的逐步回归算法和贝叶斯网络学习中的SGS算法、PC算法应用于变量选择的不足,提出了一种新的因果关系发现算法STEPCARD,并将其与STEPWISE算法和SGS算法进行了实验比较分析.实验表明,STEPCARD算法能够和SGS算法一样从初始自变量集合中找出与因变量有因果相邻关系的变量,而STEPWISE算法只能找出与因变量显著相关的变量.其次,当初始自变量集合较大,而最后输出的自变量集合较小时,STEPCARD算法的计算量比SGS算法的计算量小得多.而且,当初始自变量个数接近或大于事例个数时,SGS算法将无法应用,而STEPCARD算法依然可以得到可信的结果.     

高维数据挖掘中特征选择的稳健方法

针对高维数据的特点,即数据中变量个数往往大于样本观测数目,并且数据往往具有异质性特点,基于众数回归分析和变量选择降维技术,提出了一种稳健有效的特征选择方法,利用局部二次逼近算法(LQA)和最大期望(EM)算法,给出估计算法和最优调节参数的选取方法。通过实验的模拟数据分析表明,所提出的特征提取选择方法整体优于基于最小二乘和中位数的正则化估计方法,特别当误差是非正态分布时,与已有方法相比具有较高的预测能力和稳健性。     

基于回归系数的变量筛选方法用于近红外光谱分析

提出了一种基于回归系数的变量逐步筛选方法。对光谱中各变量计算其回归系数后,按其绝对值由大到小将相应变量排列,采用PLS交互检验按前向选择法逐步选择最佳变量子集。用该方法对玉米和柴油近红外光谱数据进行分析,对玉米蛋白质、柴油十六烷值和粘度分别选择出了14、12以及30个最佳变量用于建模,所得预测结果均优于全谱变量建模的预测结果。可见本方法是一种有效实用的近红外光谱变量选择方法。     

An inferential modeling method using enumerative PLS based nonnegative garrote regression

In this paper a robust linear regression method with variable selection is proposed for predicting desirable end-of-line quality variables in complex industrial processes. The development of such prediction models is challenging because there is usually a large pool of candidate explanatory variables, limited sample data, and multicollinearity among explanatory variables. The proposed method is named as the enumerative partial least square based nonnegative garrote regression. It employs partial least square regression in enumerative manner to generate initial model coefficients and then uses a nonnegative garrote method to shrink original coefficients so that irrelevant variables can be eliminated implicitly. Analysis about the advantages of the proposed method is provided compared to existing state-of-art model construction methods. Two simulation examples as well as an industrial application in a local semiconductor factory unit are used to validate the proposed method. These examples witness substantial improvement in terms of accuracy and robustness in variable selection compared to existing methods. Specifically, for the industrial case the percentages of improvement in terms of root mean squared error is up to 24.3% compared with the previous work.     

Variable selection using Gaussian process regression-based metrics for high-dimensional model approximation with limited data

In recent years, the importance of computationally efficient surrogate models has been emphasized as the use of high-fidelity simulation models increases. However, high-dimensional models require a lot of samples for surrogate modeling. To reduce the computational burden in the surrogate modeling, we propose an integrated algorithm that incorporates accurate variable selection and surrogate modeling. One of the main strengths of the proposed method is that it requires less number of samples compared with conventional surrogate modeling methods by excluding dispensable variables while maintaining model accuracy. In the proposed method, the importance of selected variables is evaluated using the quality of the model approximated with the selected variables only. Nonparametric probabilistic regression is adopted as the modeling method to deal with inaccuracy caused by using selected variables during modeling. In particular, Gaussian process regression (GPR) is utilized for the modeling because it is suitable for exploiting its model performance indices in the variable selection criterion. Outstanding variables that result in distinctly superior model performance are finally selected as essential variables. The proposed algorithm utilizes a conservative selection criterion and appropriate sequential sampling to prevent incorrect variable selection and sample overuse. Performance of the proposed algorithm is verified with two test problems with challenging properties such as high dimension, nonlinearity, and the existence of interaction terms. A numerical study shows that the proposed algorithm is more effective as the fraction of dispensable variables is high.

     

Sparse Bayesian Variable Selection with Correlation Prior for Forecasting Macroeconomic Variable using Highly Correlated Predictors

In this paper, we propose an integrated sparse Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. The variable selection is performed through the stochastic search variable selection technique. We assign a sparse prior distribution on the regression parameters and a correlation prior distribution for the binary vector. The performance of the proposed variable selection method is illustrated in forecasting one major macroeconomic time series of the US economy. Empirical results show that in terms of absolute forecast error and log predictive likelihood, our proposed method performs better than other three methods.     

Comparison of variable selection methods for PLS-based soft sensor modeling

Data-driven soft sensors have been widely used in both academic research and industrial applications for predicting hard-to-measure variables or replacing physical sensors to reduce cost. It has been shown that the performance of these data-driven soft sensors could be greatly improved by selecting only the vital variables that strongly affect the primary variables, rather than using all the available process variables. In this work, a comprehensive evaluation of different variable selection methods for PLS-based soft sensor development is presented, and a new metric is proposed to assess the performance of different variable selection methods. The following seven variable selection methods are compared: stepwise regression (SR), partial least squares with regression coefficients (PLS-BETA), PLS with variable importance in projection (PLS-VIP), uninformative variable elimination with PLS (UVE-PLS), genetic algorithm with PLS (GA-PLS), least absolute shrinkage and selection operator (Lasso), and competitive adaptive reweighted sampling with PLS (CARS-PLS). Their strengths and limitations for soft sensor development are demonstrated by a simulated case study and an industrial case study.     

数据缺失条件下基于启发式构元的多元回归分析方法

传统的多元回归分析方法可以对缺失数据进行预测填补,但它在构造回归方程时存在自变量形式较为固定、单一等不足。为此,提出一种基于启发式构元的多元回归分析方法,通过贪婪算法找出现有变量的优化组合形式,选取若干新构变量进行回归分析,从而得到更好的拟合优度。通过对案例中小麦茎秆机械强度缺失数据信息进行仿真计算和评估,证实了方法的有效性。算例结果表明该方法运用在缺失数据预测中拥有较好的精准性。     

Simultaneous estimation and factor selection in quantile regression via adaptive sup-norm regularization

Some regularization methods, including the group lasso and the adaptive group lasso, have been developed for the automatic selection of grouped variables (factors) in conditional mean regression. In many practical situations, such a problem arises naturally when a set of dummy variables is used to represent a categorical factor and/or when a set of basis functions of a continuous variable is included in the predictor set. Complementary to these earlier works, the simultaneous and automatic factor selection is examined in quantile regression. To incorporate the factor information into regularized model fitting, the adaptive sup-norm regularized quantile regression is proposed, which penalizes the empirical check loss function by the sum of factor-wise adaptive sup-norm penalties. It is shown that the proposed method possesses the oracle property. A simulation study demonstrates that the proposed method is a more appropriate tool for factor selection than the adaptive lasso regularized quantile regression.     

基于混合准则的软测量建模辅助变量选择方法

软传感器在工业中被广泛应用于预测与产品质量密切相关的关键过程变量,这些变量很难在线测量。要建立一个高精度的软传感器,选择合适的辅助变量是至关重要的。针对这个问题,本文通过耦合训练集的BIC准则以及验证集的MSE准则得到一个混合整数非线性规划问题,并将该MINLP问题分成内外两层结构,外层采用遗传算法对二元整数变量进行寻优,内层在整数变量固定之后退化成了较易于求解的非线性规划问题。在此基础上经过进一步分析提出了基于混合准则的变量选择方法,然后将所得辅助变量子集代入BP神经网络进行软测量建模。最后,通过4组案例对所提出方法进行验证。结果表明,所提出方法建立的软测量模型具有较好的预测性能。