25种持久性有机污染物飞灰-水分配系数的定量结构性质关系研究

分别采用多元线性回归(MLR)和径向基函数人工神经网络(RBF - ANN)建立了2个不同的持久性有机污染物飞灰-水分配系数(Ksc)的定量结构性质关系(QSPR)模型,并应用留一交叉验证方法对所建立的模型进行了检验.用所建立的模型研究了25种有机污染物的飞灰-水分配系数的定量结构性质关系.结果表明:MLR模型预测的l...     

Survey of nutrients and quality assessment of crab paste by 1H NMR spectroscopy and multivariate data analysis

Crab paste is a traditional and popular food for people in the coastal area of China.Visual inspection is currently the only method of quality assessment.We developed a new analytical method of a comprehensive survey of nutrients and quality assessment for crab paste based on a combination of 1 H NMR spectroscopy with multivariate data analysis.The aqueous extract of crab paste was dominated by amino acids,sugars,carboxylic acids,nucleotides and amines(including 19 first-time-reported compounds such as choline,uracil and guanosine).Two grades of crab paste had significant compositional differences in terms of amino acids,lactate,N-acetylglutamate,choline,dimethylamine,uridine,1-methylnicotinamide and 2-pyridienmethanol.These results provided important information on the grade-dependence of crab-paste composition,and demonstrated that NMR-MDA was effective not only for the comprehensive survey of nutrients,but also for quality assessment of crab paste.     

缺失数据下多元正态模型Monte Carlo EM算法

研究含有缺失数据的多元正态模型参数的极大似然估计问题,利用Monte Carlo EM算法求得多元正态模型参数的迭代解,并证明了此迭代解收敛到最优解,且其收敛速度是二阶的.     

基于联合熵的多变量混沌时间序列相空间重构

提出了一种多变量混沌时间序列的联合熵扩维法(JEED),为多变量时间序列的预测构造了有效的模型输入向量.首先使用互信息法求混沌系统各观测变量的延迟时间;然后使用联合熵确定各分量的嵌入维数,并按最大熵选择重构分量,不断扩张相空间维数,最终使得重构向量空间包含系统的最大信息量.仿真实验表明因为JEED确定的相空间能提供丰富的信息,在其上进行的神经网络预测取得了比单变量预测方法更好的预测效果.     

柴油机另部件磨耗状态与润滑油光谱分析结果的多元判别

本文利用润滑油中磨耗金属的光谱分析结果,提出了适合国情的判断内燃机车柴油机另部件磨耗状态的多元判别法.对判别的特征因子进行了选择,对其分辨能力进行了定量估计.证明了润滑油中铁含量的变化速率是主要特征因子.报告了马氏距离判别法的判别函数及判别效果,对明显的非正常磨耗准确率达88.0%,正常磨耗和少量非正常磨耗准确率达95.5%.此外,还报告了两种简易判别法.最后,讨论了实际应用效果和今后展望.     

A spherical representation of a correlation matrix

It is common practice to perform a principal component analysis (PCA) on a correlation matrix to represent graphically the relations among numerous variables. In such a situation, the variables may be considered as points on the unit hypersphere of an Euclidean space, and PCA provides a sort of best fit of these points within a subspace. Taking into account their particular position, this paper suggests to represent the variables on an optimal three-dimensional unit sphere.

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Résumé Il est classique d'utiliser une analyse en composantes principales pour représenter graphiquement une matrice de corrélation. Dans une telle situation, les variables peuvent être considérées comme des points sur l'hypersphère unité d'un espace Euclidien, et l'analyse en composantes principales permet d'obtenir une bonne approximation de ces points à l'aide d'un sous-espace Euclidien. Prenant en compte une telle situation géométrique, le présent article suggère de représenter les variables sur une sphère tri-dimensionelle optimale.

     

The Time and Cost Prediction of Tunnel Boring Machine in Tunnelling

0Introduction Thetimeandcostprediction,whicharegettingonslow ly,arealwaysthekeyfactorthatdeterminewhethertheassistantdecision makingsystemisutility.Soitisnecessarytoresearchonthetimeandcostneededinaspecialprojectand theirprobabilitydistributing,whichcouldbeusedasacredi blebasisforsigningacontractandasatooltooptimizecon structionplanandtoreducecost.SystemofOkubo[1]hasthreephases:①Checktheenvironmentsothatitissuitable forthefundamentalrequirementsinwhichtunnelboringma chine(TBM)isused;②Estim…     

求解多元函数总体极小谷峰法的验证与测试

本文介绍了对求多元函数总体极小谷峰法的基本思路,简述了算法的收敛和复杂性,并用多个典型函数进行了验证和测试.计算结果表明,相比其它算法,谷峰法简单,实用,运算速度快,对目标函数要求低,易推广应用.     

FORECASTING NIKKEI 225 INDEX WITH SUPPORT VECTOR MACHINE

Support Vector Machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare the perfor-mance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms other classification methods. Furthermore, we propose a combining model by integrating SVM with other classification methods. The combining model performs the best among the forecasting methods.     

Volatility forecasting for risk management

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub‐optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out‐of‐sample forecasting performance of various linear and GARCH‐type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decision making. Copyright © 2002 John Wiley & Sons, Ltd.