基于PCA和相位差校正法的风切变预警算法

为了提高相干激光雷达风切变预警算法的预警率,提出一种基于主成分分析（principal component analysis,PCA）和相位差校正法的风切变预警算法。首先,利用PCA对雷达回波信号进行低阶主成分向量提取以实现信号重构,提高信噪比。其次,采用基于快速傅里叶变换的相位差校正法对回波信号峰值频率进行校正,降低回波信号能量泄露。然后,分别利用旋转电机实验数据和小型飞机实验数据对所提算法进行性能评估。结果表明,采用所提算法估计相干激光雷达回波信号峰值频率的平均绝对误差为0.26 MHz,对实际低空风切变的预警率为92.31%。所提算法可以有效降低相干激光雷达回波信号频率估计误差,实现低空风切变预警。     

基于核函数主元分析的SVM建模方法及应用

为有效克服线性建模方法在非线性建模方面的不足,将核函数思想引入到主元分析方法(PCA)中,有效提取实验数据中的非线性特征信息,并将其作为支持向量机(SVM)的输入变量,建立工业过程软测量模型。该方法应用于丙烯腈聚合过程中转化率的预报,结果表明:该方法的预测精度优于PCA-SVM方法和KPCA-NN方法。     

智能控制电采暖系统的应用

对当前常用的几种采暖方式进行了分析比较,重点对节能智能控制电采暖系统的工作原理、技术特点、应用前景予以阐述.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

基于主成分回归分析的瓦斯含量预测

为了增加多元回归模型预测的精度,将主成分分析与多元回归分析相结合提出了PCA—MRA模型,并将该模型用于实际瓦斯含量预测。结果表明,PCA—MRA模型消除了输入变量之间的相关性,减少了输入变量值个数,提高了预测精度,便于实际推广和应用,为瓦斯含量预测提供一种新的途径。     

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality‐forecasting models be associated with real‐world trends in health‐related variables? Does inclusion of health‐related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle‐related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models. Copyright © 2014 John Wiley & Sons, Ltd.     

对称熵损失下两个指数总体均值的序约束估计

在对称熵损失下, 讨论了样本容量相等时, 两个指数总体均值λ_i(i=1,2)的约束极大似然估计i的险, 其中约束为λ₁≤λ₂. 证明了λ₁与λ₂具有比经典极大似然估计X₁与X₂ 更小的风险, 并给出了当λ₂/λ₁→∞和n→∞时,λ_i对X_i(i=1,2)渐近功效e(λ_i,X_i)的值.     

半相依回归系统参数的c-k型改进估计

针对设计矩阵Xi呈病态时的半相依线性回归系统,提出了系统参数iβ的一种c-k型改进估计,并证明了这种估计在均方误差意义下的若干优良性质.     

非均质三角形平板的惯量主轴

刚体的惯量主轴在工程上有重要的应用,从计算上确定没有对称性三角形平板的惯量主轴一般较繁杂,如果与通常求取惯量主轴的方法相反,先求出非均质任意三角形对质心的转动惯量,从转动惯量表达式可以容易给出它的中心惯量主轴。     

辨识动态系统噪声方差Q和R的新方法

对于带未知噪声方差的线性离散定常随机系统,引入左素分解可得到一个新的观测过程,它用两个滑动平均（MA）过程之和表示。用解相关函数矩阵方程组得到了噪声方差Q和R的估值器,进而基于新的观测过程的采样相关函数及其遍历性可得到噪声方差Q和R的强一致估计。算法简单,便于实时应用。一个目标跟踪系统的仿真例子说明了其有效性。