Penalized total least squares method for dealing with systematic errors in partial EIV model and its precision estimation

When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.

Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.

Comparison of Calibration Curve Method and Partial Least Square Method in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

The Laser Induced Breakdown Spectroscopy (LIBS) is a fast, non-contact, no sample preparation analytic technology;it is very suitable for on-line analysis of alloy composition. In the copper smelting industry, analysis and control of the copper alloy concentration affect the quality of the products greatly, so LIBS is an efficient quantitative analysis tech- nology in the copper smelting industry. But for the lead brass, the components of Pb, Al and Ni elements are very low and the atomic emission lines are easily submerged under copper complex characteristic spectral lines because of the matrix effects. So it is difficult to get the online quantitative result of these important elements. In this paper, both the partial least squares (PLS) method and the calibration curve (CC) method are used to quantitatively analyze the laser induced breakdown spectroscopy data which is obtained from the standard lead brass alloy samples. Both the major and trace elements were quantitatively analyzed. By comparing the two results of the different calibration method, some useful results were obtained: both for major and trace elements, the PLS method was better than the CC method in quantitative analysis. And the regression coefficient of PLS method is compared with the original spectral data with background interference to explain the advantage of the PLS method in the LIBS quantitative analysis. Results proved that the PLS method used in laser induced breakdown spectroscopy was suitable for simultaneous quantitative analysis of different content elements in copper smelting industry.

Factors Affecting Box Office during Broad Spring Festival Based on Partial Least Squares Regression

The box office during the later Spring Festival shows an attractive prospect.This paper studied the factors affecting total box office during the broad Spring Festival which is from the Spring Festival to the Lantern Festival.Data of films released during the broad Spring Festival from the years 2016 to 2019 in China were gathered,and the impact of eight explanatory variables on the box office during the broad Spring Festival was empirically analyzed by partial least squares(PLS)regression with software SIMCA.The results suggest that word-of-mouth has the most positive effect on the box office during the broad Spring Festival.Later propaganda has a positive effect,while early promotion has a negative effect on the box office.Director’s influence has a positive effect,while actor’s influence does not contribute much to the box office.Length of the trailer has a negative effect.The film format of 2D or 3D doesn’t contribute much to the box office.

Characterizing and estimating rice brown spot disease severity using stepwise regression,principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

Correlation analysis and partial least square modeling to quantify typical minerals with Chang'E-3 visible and near-infrared imaging spectrometer's ground validation data

In 2013, Chang'E-3 program will develop lunar mineral resources in-situ detection. A Visible and Near-infrared Imaging Spectrometer(VNIS) has been selected as one payload of CE-3 lunar rover to achieve this goal. It is critical and urgent to evaluate VNIS' spectrum data quality and validate quantification methods for mineral composition before its launch. Ground validation experiment of VNIS was carried out to complete the two goals, by simulating CE-3 lunar rover's detection environment on lunar surface in the laboratory. Based on the hyperspectral reflectance data derived, Correlation Analysis and Partial Least Square(CA-PLS) algorithm is applied to predict abundance of four lunar typical minerals(pyroxene, plagioclase, ilmenite and olivine) in their mixture. We firstly selected a set of VNIS' spectral parameters which highly correlated with minerals' abundance by correlation analysis(CA), and then stepwise regression method was used to find out spectral parameters which make the largest contributions to the mineral contents. At last, functions were derived to link minerals' abundance and spectral parameters by partial least square(PLS) algorithm. Not considering the effect of maturity, agglutinate and Fe0, we found that there are wonderful correlations between these four minerals and VNIS' spectral parameters, e.g. the abundance of pyroxene correlates positively with the mixture's absorption depth, the value of absorption depth added as the increasing of pyroxene's abundance. But the abundance of plagioclase correlates negatively with the spectral parameters of band ratio, the value of band ratio would decrease when the abundance of plagioclase increased. Similar to plagioclase, the abundance of ilmenite and olivine has a negative correlation with the mixture's reflectance data, if the abundance of ilmenite or olivine increase, the reflectance values of the mixture will decrease. Through model validation, better estimates of pyroxene, plagioclase and ilmenite's abundances are given. It is concluded that VNIS has the capability to be applied on lunar minerals' identification, and CA-PLS algorithm has the potential to be used on lunar surface's in-situ detection for minerals' abundance prediction.

An Improved PLS (IPLS) Method Utilizing Local Standardization Strategy for Multimode Process Monitoring

Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Boosting the partial least square algorithm for regression modelling

Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.

美国户外休闲产业发展特征及预测模型构建研究——基于PLS和PLS-DA方法的分析

户外休闲产业是美国经济的支柱性产业,分析其发展特征和构建预测模型对于我国健身休闲产业健康、可持续发展有重要借鉴价值。研究收集了美国50个州2021年户外休闲产业相关数据,选取27个影响指标,运用PLS和PLS-DA方法,对美国户外休闲产业发展特征进行分析并构建预测模型。研究表明:(1)美国州域户外休闲产业可分为4种发展模式,即总量大占比小型、总量小占比大型、总量较大占比较小型、总量较小占比较大型,4种发展模式在空间上呈现一定集聚分布特征。(2)构建的预测模型有效且精度较高,并筛选出12个VIP指标,经济、人口和社会因素似乎更多地影响美国户外休闲产业发展,而自然环境因素的影响相对较弱。

基于PLS-SEM的航天器控制系统能力建模方法

为提升航天任务完成品质,航天器需根据任务及环境针对性调整自身能力,而对航天器控制系统高层次能力的定量刻画,即系统能力建模是实现上述调整的重要理论依据。针对一类航天器姿态控制系统,提出一种基于偏最小二乘-结构方程模型(partial least square structural equation model,PLS-SEM)的航天器控制系统能力建模方法,实现对包括控制能力、观测能力等抽象能力的定量描述。首先,根据航天器闭环控制系统的结构要素,综合设计能力建模所需的指标类型,生成建模数据样本。在此基础上,设计并构建SEM框架下的能力变量体系,进而通过PLS算法完成模型路径、载荷、权重等关键参数的确定,并对所得PLS-SEM能力模型的结构方程与测量方程的有效性、可信性等分别进行评估。最终,根据航天器PLS-SEM能力模型对控制系统的各抽象能力进行定量描述与分析,验证本文所提出建模方法的可行性。

基于HPLC-QAMS多指标成分联合OPLS-DA及加权TOPSIS模型的威灵仙药材质量评价

目的建立不同产地威灵仙中9个成分含量同步检测方法,筛选影响其质量的差异标志物,对其质量差异性进行评价。方法对8省16个批次威灵仙样品进行回流提取,提取物采用HPLC法检测;采用化学识别模式和加权TOPSIS法建立威灵仙质量优劣评价模型,对其质量差异性进行综合评价。结果灵仙新苷、虎掌草皂甙D、威灵仙皂甙C、虎掌草皂苷B、常春藤皂苷元、齐墩果酸、3,5,6,7,8,3',4'-七甲氧基黄酮、橙皮素和芒柄花素分别在1.27~31.75、4.48~112.00、7.35~183.75、3.69~92.25、6.16~154.00、20.95~523.75、0.58~14.50、0.39~9.75和0.26~6.50µg/mL范围内线性关系良好(r>0.9990),平均加样回收率为96.91%~100.12%,RSD为0.71%~1.53%(n=9);16批样品聚为3类;齐墩果酸、威灵仙皂甙C、灵仙新苷和常春藤皂苷元可能是影响威灵仙产品质量主要潜在标志物;加权TOPSIS法分析结果显示16批威灵仙质量评价贴近度在0.0978~0.8182之间,其中S13最大(0.8182)。结论建立的威灵仙中9种成分定量分析方法,操作简便、结果准确;化学计量学及加权TOPSIS法可用于评价其质量差异性。

基于MW-MKEPLS的多重时变间歇生产过程质量预测

间歇生产过程的多重时变特性和非线性使得质量预测问题变得复杂。为了提高间歇过程质量预测精度,提出了滑动窗多向核熵偏最小二乘(moving window multiway kernel entropy partial least squares,MW-MKEPLS)方法。首先采用滑动窗进行数据的动态更新获取,构建了滑动窗多重时变模型;然后在滑动窗多重时变模型下通过核函数将数据映射到高维特征空间,采用Renyi熵贡献度进行数据特征提取,更好地获取数据的信息熵和非线性;最后在KECA处理后的高维特征空间进行质量预测。通过青霉素生产发酵过程进行了实验验证,并与MKPLS和MKEPLS进行对比分析,结果表明所提方法的质量预测精度更高。

A Radial Basis Function Method with Improved Accuracy for Fourth Order Boundary Value Problems

Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating higher order derivatives with Radial Basis Function methods. The method is used to solve fourth order boundary value problems. The use and location of ghost points are examined in order to enforce the extra boundary conditions that are necessary to make a fourth-order problem well posed. The use of ghost points versus solving an overdetermined linear system via least squares is studied. For a general fourth-order boundary value problem, the recommended approach is to either use one of two novel sets of ghost centers introduced here or else to use a least squares approach. When using either ghost centers or least squares, the random variable shape parameter strategy results in significantly better accuracy than when a constant shape parameter is used.

偏最小二乘法(PLS)及其在分析化学中的应用

本文介绍了一种性能优于其他多元统计方法的新的多元校准法——偏最小二乘法(PLS),研究了其理论基础、算法和性能,评述了其在吸收光谱分析、荧光分光光度分析、ICP—AES、色谱分析、核磁共振谱分析、生物化学、产品质量预测、毒理学、环境化学和地球化学研究等方面的应用以及应用发展动向。引用文献58篇。

PLS-BP法近红外光谱定量分析研究

建立BP模型用于近红外光谱定量分析时,为克服所建模型与训练样本集产生“过拟合”,先用线性算法为其压缩训练数据是必要的。目前多采用主成分法(PCA)和逐步回归法(SRA)。主成分法具有极强的压缩数据能力,用它压缩成的主成分输入BP网所建模型的预测精度一般能满足要求,但它处理数据时未考虑输出变量的影响。逐步回归法根据系统输出选择变量,但所选变量具有自相关性,而且与训练集样品的排列顺序有关,很难选出最好的变量,往往难满足预测精度要求。本研究用偏最小二乘法(PLS),根据输出变量将原始数据压缩为主成分,输入BP网并用所建模型预测30个小麦样品的蛋白质含量。结果表明,与PCA-BP模型的预测决定系数(R2)从92.50提高到97.10,训练迭代次数从12 000减少到4 500。

PLS法在同时分光光度测定痕量金属离子中的应用

本文研究了新显色剂4-(2-苯并噻唑偶氮)邻苯二酚-Mo(Ⅵ)、W(Ⅶ)-二苯胍三元配合物的高灵敏显色反应以及偏最小二乘法(PLS)在同时分光光度测定痕量钼和钨中的应用。与共轭梯度法和线性规划法相比,PLS法的运行时间短,结果更准确可靠,尤其适用于处理成批试样,为带微处理机的分光光度计提供了一种新的计算方法。

荧光光谱PLS法同时测定氨基酸混合物

根据酪氨酸、色氨酸和苯丙氨酸具有荧光 ,且荧光光谱相互重叠的性质 本文用偏最小二乘法 (PLS)解析 3种氨基酸的荧光光谱 ,克服了组分之间的重叠及非线性干扰 ,建立了一种新的同时测定 3种氨基酸分析方法 用于混合样中色氨酸、酪氨酸和苯丙氨酸的分析 。