模糊线性回归模型的约束最小二乘估计

自Tanaka等1982年提出模糊回归概念以来,该问题已得到广泛的研究。作为主要估计方法之一的模糊最小二乘估计以其与统计最小二乘估计的密切联系更受到人们的重视。本文依据适当定义的两个模糊数之间的距离,提出了模糊线性回归模型的一个约束最小二乘估计方法,该方法不仅能使估计的模糊参数的宽度具有非负性而且估计的模糊参数的中心线与传统的最小二乘估计相一致。最后,通过数值例子说明了所提方法的具体应用。     

混合位级正交试验设计的极差分析方法

本文指出，在混合位级的正交试验设计中，以往的极差分析方法存在着不足。本文给出了极差的修正方法，并用大量实例做了数值验证，说明这个修正确实很有效     

偏最小二乘-同步荧光法测定人尿中黄蝶呤

建立了人尿中黄蝶呤含量测定的同步荧光分析方法。在pH 7.8 KH2PO4-NaOH缓冲溶液中,于Δλ为70 nm的条件下对黄蝶呤及其它蝶呤类化合物进行同步荧光扫描,所得的重叠波谱数据用主成分回归法(PCR)、偏最小二乘法(PLS)、经典最小二乘法(CLS)和径向基人工神经网络(RBF-ANN)等多元校正法进行处理,结果表明偏最小二乘法(PLS)的分析结果最好,其标准偏差为4.29%。该方法简便、快速、准确,避免了较繁琐的样品前处理过程,应用于人尿中黄蝶呤分析,结果令人满意。     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

偏最小二乘法在原子吸收光谱分析中的应用——钴和铟的同时测定

本文用偏最小二乘法（ＰＬＳ）校正了火焰原子吸收分析Ｉｎ２５２．１３７ｎｍ对Ｃｏ２５２．１３６ｎｍ的吸收线重叠干扰,对混合样中Ｃｏ和Ｉｎ的含量进行了测定,结果令人满意。     

Chapter 11 Simulation studies of efficiency,returns to scale and misspecification with nonlinear functions in DEA

Using statistically designed experiments, 12,500 observations are generated from a 4-pieced Cobb-Douglas function exhibiting increasing and decreasing returns to scale in its different pieces. Performances of DEA and frontier regressions represented by COLS (Corrected Ordinary Least Squares) are compared at sample sizes ofn=50, 100, 150 and 200. Statistical consistency is exhibited, with performances improving as sample sizes increase. Both DEA and COLS generally give good results at all sample sizes. In evaluating efficiency, DEA generally shows superior performance, with BCC models being best (except at corner points), followed by the CCR model and then by COLS, with log-linear regressions performing better than their translog counterparts at almost all sample sizes. Because of the need to consider locally varying behavior, only the CCR and translog models are used for returns to scale, with CCR being the better performer. An additional set of 7,500 observations were generated under conditions that made it possible to compare efficiency evaluations in the presence of collinearity and with model misspecification in the form of added and omitted variables. Results were similar to the larger experiment: the BCC model is the best performer. However, COLS exhibited surprisingly good performances — which suggests that COLS may have previously unidentified robustness properties — while the CCR model is the poorest performer when one of the variables used to generate the observations is omitted.     

主成份分析-偏最小二乘法分光光度法同时测定钴、镍、铜、锌四组份体系

用 PCA 及 PLS 分步处理分光光度数据,有效降低了计算的维数。以该法对不同酸度体系的5-Br-PADAP-钴、镍、铜、锌分光光度数据进行了处理,定量分析结果良好。     

Extension and application of multivariate curve resolution-alternating least squares to four-way quadrilinear data-obtained in the investigation of pollution patterns on Yamuna River,India—A case study

This study focuses on the development and extension of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to the analysis of four-way datasets. The proposed extension of the MCR-ALS method with non-negativity and the newly developed quadrilinear constraints can be exploited to summarize and manage huge multidimensional datasets and resolve their four way component profiles. In this study, its application is demonstrated by analyzing a four-way data set obtained in a long term environmental monitoring study (15 sampling sites × 9 variables × 12 months × 7 years) belonging to the Yamuna River, one of the most polluted rivers of India and the largest tributary of the Ganges river. MCR-ALS resolved pollution profiles described appropriately the major observed changes on pH, organic pollution, bacteriological pollution and temperature, along with their spatial and temporal distribution patterns for the studied stretch of Yamuna River. Results obtained by MCR-ALS have also been compared with those obtained by another multi-way method, PARAFAC. The methodology used in this study is completely general and it can be applied to other multi-way datasets.     

Muscle oxygenation measurement in humans by noninvasive optical spectroscopy and Locally Weighted Regression

We have developed a method to make real-time, continuous, noninvasive measurements of muscle oxygenation (Mox) from the surface of the skin. A key development was measurement in both the visible and near infrared (NIR) regions. Measurement of both oxygenated and deoxygenated myoglobin and hemoglobin resulted in a more accurate measurement of Mox than could be achieved with measurement of only the deoxygenated components, as in traditional near-infrared spectroscopy (NIRS). Using the second derivative with respect to wavelength reduced the effects of scattering on the spectra and also made oxygenated and deoxygenated forms more distinguishable from each other. Selecting spectral bands where oxygenated and deoxygenated forms absorb filtered out noise and spectral features unrelated to Mox. NIR and visible bands were scaled relative to each other in order to correct for errors introduced by normalization. Multivariate Curve Resolution (MCR) was used to estimate Mox from spectra within each data set collected from healthy subjects. A Locally Weighted Regression (LWR) model was built from calibration set spectra and associated Mox values from 20 subjects using 2562 spectra. LWR and Partial Least Squares (PLS) allow accurate measurement of Mox despite variations in skin pigment or fat layer thickness in different subjects. The method estimated Mox in five healthy subjects with an RMSE of 5.4%.     

Broadcasting, Scalability, and Reconfigurability Aspects in an All-Optical Network Architecture

All-optical networks are a sound answer to the huge data traffic demand expected for the forthcoming information society. Interesting networking aspects are the broadcasting, scalability, and reconfiguration in an all-optical environment. These issues are addressed in the present article for an all-optical network called MATRIX, which is time slotted and uses packet switching. The architecture enables the all-optical interconnection of a large number of nodes with both a small number of wavelengths and wavelength continuity based on a consequent exploitation of wavelength-division multiplexing and space-division multiplexing (i.e., multiple fibers per cable are used). For an efficient use of the resources, each node is equipped with multiple transmitters and receivers, respectively. Two different broadcasting schemes are investigated. An analytical model is developed, and the corresponding simulation results are in good agreement. It is further shown how different networks of arbitrary size can be interconnected to larger all-optical network clusters, thereby providing scalability not only with respect to the number of nodes but also to the geographical extension. Finally, wavelength reassignments allow change of optical paths through the network in case of changing traffic patterns or network failures in order to best exploit the available network resources. The total number of possible configurations is determined by the theory of Latin Squares.