一种用于二类样本判别分析的PLS方法

提出了一种新的用于两类样本判别分析问题的ＰＬＳ方法，该法对响应函数ｙ作了类似神经网络算法中用的Ｓｉｇｎｏｉｄ函数转换，可用一种新的优化目标判据来提取一组ＰＬＳ方法中两两正交的隐变量ｔ１，ｔ２．．．，用这些变量可构成判别分类图，并可得到比较理想的判别方向矢量。     

多元非线性荧光校正的人工神经网络方法

由实验测定得知罗丹明Ｂ，丁基罗丹明Ｂ，曙红Ｂ组成的三组分混和荧光分析体系存在严重的荧光熄灭现象，其混和物的荧光光谱呈非线性，ＰＬＳ难以校正。本文成功地将ＢＰ－ＡＮＮ应用于此多元非线性荧光校正问题，完成了三组分的同时测定，得到了满意的结果。     

THE NON－TEMPLATE SYNTHESIS OF MACROCYCLIC SCHIFF BASE DERIVED FROM THIOPHENE，AND ITS Ag（Ⅰ），Hg（Ⅱ） COMPLEXES

ＴＨＥ　ＮＯＮ－ＴＥＭＰＬＡＴＥ　ＳＹＮＴＨＥＳＩＳ　ＯＦ　ＭＡＣＲＯＣＹＣＬＩＣ　ＳＣＨＩＦＦ　ＢＡＳＥ　ＤＥＲＩＶＥＤ　ＦＲＯＭ　ＴＨＩＯＰＨＥＮＥ，ＡＮＤ　ＩＴＳ　Ａｇ（Ⅰ），Ｈｇ（Ⅱ）　ＣＯＭＰＬＥＸＥＳＴＨＥＮＯＮ－ＴＥＭＰＬＡＴＥＳＹＮＴ...     

甲氧苄胺嘧啶药物的非破坏分析

将偏最小二乘（ＰＬＳ）法同近红外漫反射光谱法结合，非破坏分析了粉末药品甲氧苄胺嘧啶。讨论了波长对ＰＬＳ定量预报能力的影响。校正样品和预测样品的预测结果相对标准误差分别为０．３３％和１．３９％。     

SYNTHESIS,CRYSTAL STRUCTURE AND MAGNETISM OF THE COMPLEX:GdFe(CN)_64H_2O

ＳＹＮＴＨＥＳＩＳ，ＣＲＹＳＴＡＬ　ＳＴＲＵＣＴＵＲＥ　ＡＮＤ　ＭＡＧＮＥＴＩＳＭ　ＯＦ　ＴＨＥ　ＣＯＭＰＬＥＸ：ＧｄＦｅ（ＣＮ）_６４Ｈ_２ＯＳＹＮＴＨＥＳＩＳ，ＣＲＹＳＴＡＬＳＴＲＵＣＴＵＲＥＡＮＤＭＡＧＮＥＴＩＳＭＯＦＴＨＥＣＯＭＰＬＥＸ：ＧｄＦ...     

气—固流化床反应器内双流体力学模型及其验证

从湍流两相流理论出发，详细推导了描述气－固流化床内两相流动的双流体力学模型，根据方程的封闭性原理给出了所需构关系的表达式；针对模型方程的非线性，耦合性和形式相同等特点，集合气－固流化床内εｇ＋εｓ＝１及εｓ〈εｓ，ｍａｘ的限制，在数值计算上提出了改进的ＳＩＭＰＬＥ算法。在微型计算机上开发了ＣＡＳＩＣＣ软件包，其计算程度在ＮＤＰ－Ｆｏｒｔｒａｎ环境下执行，可以给出了稳态或非稳态的二维直角坐标系或坐标     

NONTEMPLATE SYNTHESIS OF TWO N_2O_2S MACROCYCLES DERIVED FROM THIOPHENE，AND Hg（Ⅱ）， Pb（Ⅱ）， Cd（Ⅱ） COMPLEXES

ＮＯＮＴＥＭＰＬＡＴＥＳＹＮＴＨＥＳＩＳＯＦＴＷＯＮ_２Ｏ_２ＳＭＡＣＲＯＣＹＣＬＥＳＤＥＲＩＶＥＤＦＲＯＭＴＨＩＯＰＨＥＮＥ，ＡＮＤＨｇ（Ⅱ），Ｐｂ（Ⅱ），Ｃｄ（Ⅱ）ＣＯＭＰＬＥＸＥＳ￥ＬｉｎｇＺＨＡＯ；ＦｕＸｉｎＸＩＥ；ＺｈｉＱｉａｎｇＸＵ；Ｇｕａ...     

含内环化与不含内环化固化理论间的对应关系

含内环化与不含内环化固化理论间的对应关系黄旭日，肖兴才，李泽生，孙家，唐敖庆（吉林大学理论化学研究所，长春，１３００２３）关键词内环化，非线性缩聚反应，数量分布函数，高分子矩在研究非线性缩聚反应体系的固化问题时，Ｆｌｏｒｙ和Ｓｔｏｃｋｍａｙｅｒ等［１...     

人工神经网络-伏安分析法同时测定邻、间、对二硝基苯

将反向传播算法的前馈神经网络用于导数脉冲伏安分析法同时测定邻、间、对二硝基苯。实验在盐酸－氯化钾－乙醇介质中进行，悬汞电极作为工作电极。通过对网络结构和参数的优化，加快了训练速度，提高了预测的准确度。用该法对邻、间、对二硝基苯混合物进行定量分析，预测的相对标准误差（ＳＥＰ）分别为４２６％，４９９％和４８６％。对人工神经网络（ＡＮＮ）和偏最小二乘法（ＰＬＳ）的结果进行的比较表明，ＡＮＮ法优于ＰＬＳ法。     

NOVEL PEROXO COMPLEXES OF RARE EARTH METALS CONTAINING 1,10-PHENANTHROLINE AS A COLIGAND

ＮＯＶＥＬ　ＰＥＲＯＸＯ　ＣＯＭＰＬＥＸＥＳ　ＯＦ　ＲＡＲＥ　ＥＡＲＴＨ　ＭＥＴＡＬＳ　ＣＯＮＴＡＩＮＩＮＧ　１，１０－ＰＨＥＮＡＮＴＨＲＯＬＩＮＥ　ＡＳ　Ａ　ＣＯＬＩＧＡＮＤＮＯＶＥＬＰＥＲＯＸＯＣＯＭＰＬＥＸＥＳＯＦＲＡＲＥＥＡＲＴＨＭＥＴＡＬＳ...     

构建支持向量机-偏最小二乘法为药物构效关系建模

为研究药物构效关系积累样本数据的过程中,需为小样本建模。此时较易造成过拟合,影响模型的预测性能和稳定性。为此可用偏最小二乘(PLS)法从样本数据中成对地提取最优成分,消除自变量间的复共线性,并有效的降维,然后应用最小二乘支持向量机对成对成分进行非线性回归,并以基于误差修正的策略调整,使之更有效地表达自、因变量间的非线性关系。由此构建为EB-LSSVM-PLS算法,所建模型的预报精度高,稳定性良好。将其应用于新型黄烷酮类衍生物的QSAR建模,效果令人满意,其泛化性能优于其它方法。     

主成分回归残差神经网络校正算法用于近红外光谱快速测定汽油辛烷值

根据汽油辛值预测体系本身的非线性特点,提出主成分回归残差神经网络校正算法（principal component regression residual artificial neural network,PCRRANN)用于近红外测定汽油辛烷值的预测模型校正,该方法给合了主成分回归算法（PC）,与经典的线性校正算法（PLS（Partial Least Square),PCR, 以及非线性PLS（NPLS,Non-linear PLS)等相比,预测明显的改善,文中还讨论了PCR主成分数及训练参数对预则模可能的影响。     

段式正交信号校正方法及在小麦近红外光谱数据分析中的应用

针对光谱数据峰宽、局部效应显著、含有噪音、变量个数多及彼此间常存在严重的复共线性等问题,改进和设计一种光谱数据局部校正方法:基于窗口平滑的段式正交信号校正方法,并将之结合偏最小二乘回归,以实现光谱数据的预处理及定量分析。通过NIPALS算法初始化将滤去的正交成分,以近邻分段方式进行逐个波长点的正交信号校正。而后将去噪后的光谱矩阵作为新的自变量阵,通过偏最小二乘回归构建其与性质参变量间的校正模型。通过小麦近红外漫反射光谱数据的应用实验结果表明,本方法正交成分估计稳定,去噪明显,模型的预报性能优于其它方法,PLS成分数减少,模型更加简洁。     

An optimization‐based undeflated PLS (OUPLS) method to handle missing data in the training set

Advances in sensory systems have led to many industrial applications with large amounts of highly correlated data, particularly in chemical and pharmaceutical processes. With these correlated data sets, it becomes important to consider advanced modeling approaches built to deal with correlated inputs in order to understand the underlying sources of variability and how this variability will affect the final quality of the product. Additional to the correlated nature of the data sets, it is also common to find missing elements and noise in these data matrices. Latent variable regression methods such as partial least squares or projection to latent structures (PLS) have gained much attention in industry for their ability to handle ill‐conditioned matrices with missing elements. This feature of the PLS method is accomplished through the nonlinear iterative PLS (NIPALS) algorithm, with a simple modification to consider the missing data. Moreover, in expectation maximization PLS (EM‐PLS), imputed values are provided for missing data elements as initial estimates, conventional PLS is then applied to update these elements, and the process iterates to convergence. This study is the extension of previous work for principal component analysis (PCA), where we introduced nonlinear programming (NLP) as a means to estimate the parameters of the PCA model. Here, we focus on the parameters of a PLS model. As an alternative to modified NIPALS and EM‐PLS, this paper presents an efficient NLP‐based technique to find model parameters for PLS, where the desired properties of the parameters can be explicitly posed as constraints in the optimization problem of the proposed algorithm. We also present a number of simulation studies, where we compare effectiveness of the proposed algorithm with competing algorithms. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimized sample-weighted partial least squares

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.     

Simultaneous kinetic-spectrophotometric determination of sulfide and sulfite by partial least squares and genetic algorithm variable selection

Simultaneous multicomponent analysis is usually carried out by multivariate calibration models such as partial least squares (PLS) that utilize the full spectrum. It has been demonstrated by both experimental and theoretical considerations that better results can be obtained by a proper selection of the spectral range to be included in calculations. A genetic algorithm is one of the most popular methods for selecting variables for PLS calibration of mixtures with almost identical spectra without loss of prediction capacity. In this work, a simple and precise method for rapid and accurate simultaneous determination of sulfide and sulfite ions based on the addition reaction of these ions with new fuchsin at pH 8 and 25°C by PLS regression and using a genetic algorithm (GA) for variable selection is proposed. The concentrations of sulfide and sulfite ions varied between 0.05–2.50 and 0.15–2.00 μg/mL, respectively. A series of synthetic solutions containing different concentrations of sulfide and sulfite were used to check the prediction ability of GA-PLS models. The root mean square error of prediction with PLS on the whole data set was 0.19 μg/mL for sulfide and 0.09 μg/mL for sulfite. After the application of GA, these values were reduced to 0.04 and 0.03 μg/mL, respectively. The text was submitted by the authors in English.     

自适应模糊偏最小二乘方法在药物构效关系建模中的应用

作为一种局部逼近方法,自适应神经模糊推理系统(ANFIS)适于为药物定量构效关系(QSAR)建模。描述药物分子结构的参数较多,常存在耦合关系,会增加建模难度,并影响模型的预报性能。为此,将ANFIS和偏最小二乘(PLS)相结合,先由PLS从样本数据中提取成分,再由ANFIS实现每对成分间的非线性映射,并基于输出误差进一步修正所提取的成分,使之对因变量具有最优的解释能力,由此构建为EB-AFPLS方法。该法已成功地应用于HIV-1蛋白酶抑制剂的QSAR建模,效果良好,显示出很强的学习能力,所建模型的预报性能也优于其它方法。     

Incorporating variable importance into kernel PLS for modeling the structure–activity relationship

Kernel partial least squares (KPLS) has become popular techniques for chemical and biological modeling, which is a nonlinear extension of linear PLS. Training samples are transformed into a feature space via a nonlinear mapping, and then PLS algorithm can be carried out in the feature space. However, one of the main limitations of KPLS is that each feature is given the same importance in the kernel matrix, thus explaining the poor performance of KPLS for data with many irrelevant features. In this study, we provide a new strategy incorporated variable importance into KPLS, which is termed as the WKPLS approach. The WKPLS approach by modifying the kernel matrix provides a feasible way to differentiate between the true and noise variables. On the basis of the fact that the regression coefficients of the PLS model reflect the importance of variables, we firstly obtain the normalized regression coefficients by establishing the PLS model with all the variables. Then, Variable importance is incorporated into primary kernel. The performance of WKPLS is investigated with one simulated dataset and two structure–activity relationship (SAR) datasets. Compared with standard linear kernel PLS and Gaussian kernel PLS, The results show that WKPLS yields superior prediction performances to standard KPLS. WKPLS could be considered as a good mechanism by introducing extra information to improve the performance of KPLS for modeling SAR.     

Variable selection in multivariate calibration based on clustering of variable concept

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.