SPSS软件在变量筛选中的应用

介绍了SPSS软件的基本原理,通过应用高级统计命令——复回归分析,突出其在进行变量筛选时,对大数据量处理的优势,并求出其回归方程式,以便进行准确的统计预测。     

济南高新开发区中心广场规划设计

通过对济南高新区中心广场规划设计的分析 ,提出在城市中心广场设计中应结合特定的环境条件 ,树立明确的规划指导思想及设计理念 ,建立以功能结构、道路系统、景观空间及绿化系统为构架的理论体系 ,从而对规划设计的全过程进行整体性的理论指导     

环口板加固T型方钢管节点在轴压作用下极限承载力的参数分析

为了研究环口板加固对T型方钢管节点在支管承受轴向压力作用下的极限承载力的影响,本文运用前期工作中所采用的有限元模型建立方法,对320个环口板加固T型方钢管节点进行了参数分析。参数包括支管和主管的宽度比β,主管的宽度与厚度比2γ,环口板与主管厚度比τc和环口板与支管宽度比lc/d1。参数分析结果表明:通过对环口板的宽度和厚度进行适当的组合,可以显著提高节点的承载力。然后将有限元模拟得到的极限承载力结果与前期工作中推导出的极限承载力公式的计算结果进行对比,给出了极限承载力计算公式的适用范围。     

基于改进灰色模型的电力负荷中长期预测

基于电力系统中长期负荷预测的特点,针对常规灰色预测模型存在的不足,提出一种基于数据平滑处理,以及线性回归残差修正的改进灰色预测方法。对某地区算例比较和分析表明,本方法可明显提高中长期负荷预测精度。     

Identification of heavy metal-contaminated Tegillarca granosa using laser-induced breakdown spectroscopy and linear regression for classification

Tegillarca granosa (T. granosa) is susceptible to heavy metals, which may pose a threat to consumer health. Thus, healthy and polluted T. granosa should be distinguished quickly. This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy (LIBS) coupled with linear regression classification (LRC). Five types of T. granosa were studied, namely, Cd-, Zn-, Pb-contaminated, mixed contaminated, and control samples. Threshold method was applied to extract the significant variables from LIBS spectra. Then, LRC was used to classify the different types of T. granosa. Other classification models and feature selection methods were used for comparison. LRC was the best model, achieving an accuracy of 90.67%. Results indicated that LIBS combined with LRC is effective and feasible for T. granosa heavy metal detection.     

利用均匀设计试验进行混凝土配合比优化

提出了混凝土配合比试验的均匀设计方法,采用逐步回归法对试验数据进行回归拟合,以经济成本为目标函数,利用目标规划的方法进行混凝土配合比优化．     

截尾数据下一类半参数回归模型的估计

讨论了一类半参数回归模型y =x′β+g(t′α) +e .假定y被随机变量T右侧截尾 ,T与y独立 ,T～G。在G已知和未知两种情况下 ,构造了α、β和g(·) 的强相合估计     

The improved neutral network and its application for valuing rock mass mechanical parameter

The artificial neutral network（ANN） has the ability that self-study and self-remember, its 3 layers BP network has been applied extensively, but sometimes because of serious multi-correlation between the variables, and a few observations while many variables, there usually will result into paralyzing in study, and the neutral network further development is restricted in the system to some extent. The partial least square regression（PLS） has its advantage of building the calculation model between the variables with strong multi-correlation, especially much effective on a few data and many variables. So a new and effective method-improved neutral network has been introduced-the neutral network based on the PLS. The results of example show the improved method has a few calculations and high accuracy, and provide a new way for valuing the rock mass mechanical parameters.     

Business intelligence success: The roles of BI capabilities and decision environments

This study examines the role of the decision environment in how well business intelligence (BI) capabilities are leveraged to achieve BI success. We examine the decision environment in terms of the types of decisions made and the information processing needs of the organization. Our findings suggest that technological capabilities such as data quality, user access and the integration of BI with other systems are necessary for BI success, regardless of the decision environment. However, the decision environment does influence the relationship between BI success and capabilities, such as the extent to which BI supports flexibility and risk in decision making.     

A Recursive Orthogonal Least Squares Algorithm for Training RBF Networks

A recursive orthogonal least squares (ROLS) algorithm for multi-input, multi-output systems is developed in this paper and is applied to updating the weighting matrix of a radial basis function network. An illustrative example is given, to demonstrate the effectiveness of the algorithm for eliminating the effects of ill-conditioning in the training data, in an application of neural modelling of a multi-variable chemical process. Comparisons with results from using standard least squares algorithms, in batch and recursive form, show that the ROLS algorithm can significantly improve the neural modelling accuracy. The ROLS algorithm can also be applied to a large data set with much lower requirements on computer memory than the batch OLS algorithm.