主成分分析映射技术在交互模式识别中应用的探讨

根据科学决策和科学管理的理论，了主成分分析映射技术的原理和在交互模式识别中的应用。     

高精度OFDM信道估计算法

为了降低OFDM系统经典最小均方误差(MMSE)信道估计算法的误码率(BER),提出了经带阻滤波的MMSE信道估计算法.该算法首先采用MMSE得到初始的信道估计值,再利用离散傅里叶变换的特性,将噪声和信道频率响应信息分离开来,最后通过带阻滤波器对估计算法进行滤波,进而提高系统的BER性能.仿真结果表明,在增加少量计算复杂度的情况下,在导频间隔保持不变或逐渐增加两种情况下,所提算法的估计性能始终优于经典MMSE信道估计算法.     

影响氧化钴主成分的因素及其控制

简述了氧化钴的生产流程，着重分析了煅烧过程影响氧化钴主成分的因素，及其控制方法，以确保氧化钴的质量。     

2种叶型元胡叶片的显微特征及主成分分析

[目的]比较2种叶型的元胡(Corydalis yanhusuo W. T. Wang)(大叶元胡与小叶元胡)叶片在显微结构上的区别,为元胡的分类提供相关依据.[方法]采用石蜡切片法和临时装片法分别制作元胡的叶片横切片和表皮细胞临时装片,利用光学显微镜进行观察,并对其显微特征参数进行主成分分析.[结果]大叶元胡的叶片和叶肉组织厚度显著高于小叶元胡;其维管组织在数量上和大小上均优于小叶元胡;大叶元胡叶下表皮细胞垂周壁为深波状,气孔器呈椭圆形,小叶元胡下表皮细胞垂周壁为平直状,气孔器呈近圆形;大叶元胡的主成分值显著高于小叶元胡.[结论]大、小叶元胡叶片的显微结构存在显著差异.     

基于主成分分析法的土壤肥力评价

[目的]为潢川县粮食生产及作物施肥技术提供依据.[方法]采用网格布点法取样322个进行化学分析,然后采用主成分分析法进行评价.[结果]全县土壤全氮含量为0.48～2.77 g/kg,全磷含量为0.15～0.86 g/kg,有机质含量为6.00～41.80 g/kg,速效磷含量为0.10～68.90 mg/kg,速效钾含量为21.10～255.90 mg/kg,缓效钾含量为15.40～1 539.70 mg/kg,pH值范围为4.93～7.95,阳离子交换量为0.40～31.20 cmol/kg;将土壤肥力分级,依次为:一等地占0.9%,二等地占5.3%,三等地占40.1%,四等地占49.4%,五等地占4.3%.[结论]中低产田合计占53.7%,需进一步采取改良措施.     

基于主成分分析法的我国农村居民消费结构的综合评价

为研究农村居民的消费结构,根据<中国统计年鉴2010>中的数据,对2009年我国各地区农村居民家庭平均每人生活消费支出作主成分分析.提取了1个主成分,根据主成分得分对全国31个省(自治区、直辖市)进行排序,上海得分最高,排名第1;东南部沿海省份、东北以及北京、天津排名靠前;以河北、山西、湖北等为代表的华北、华中地区主成分得分为负,略低于全国平均水平;贵州、西藏、甘肃等西部地区排名最后.并对各地区农村居民消费结构进行分析,给出了促进农村居民消费的建议.     

适用于快衰落环境的OFDM/TDM信道估计算法

针对正交频分复用/时分复用(OFDM/TDM)系统现有导频结构和信道估计算法缺乏对信道快衰落实时跟踪能力的问题,提出一种改进信道估计算法.该算法利用相邻2个时域复用导频序列对信道特性进行估计得到信道传递函数,通过内插处理获得数据符号的信道状态信息,并利用最小均方误差准则的频域均衡算法补偿信道衰落.仿真结果表明,与现有信道估计算法相比,在相同数据传输效率情况下,提出算法不仅能够适用于慢衰落信道,同时对快衰落信道的实时变化有良好的跟踪能力,改善系统性能.     

江西省水资源承载力的主成分分析研究

利用SPSS13．0软件,对江西省水资源承载力的主要影响因素进行主成分分析研究,建立水资源承载力回归模型。根据江西省1999～2007年的统计数据以及江西省“十一五”规划,预测2010年江西省可承载的工业废水排放量为6．69亿t,比2007年降低6．3％.     

基于核主成分分析与最小二乘支持向量机结合处理时间序列预测问题

探讨了最小二乘支持向量机时间序列预测的方法,提出了用核主成分分析提取主元,然后用最小二乘支持向量机进行预测．通过实验表明,这种方法得到的效果优于没有特征提取的预测．同时与主成分分析提取特征相比,用核主成分分析效果更好．     

基于主成分分析法的农业科技网站评价方法研究

农业科技网站是服务"三农"和推进农业信息化的重要载体.通过构建农业科技网站评价模型,并利用主成分分析法进行分析,最后运用该模型使用SPSS 13.0对18个农业科技网站进行了综合排名,并根据评估结果对农业科技网站进行了分析评价,为农业科技网站建设提供参考.     

Nonlinear principal components analysis: Introduction and application.

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover nonlinear relationships between variables. Also, nonlinear PCA can deal with variables at their appropriate measurement level; for example, it can treat Likert-type scales ordinally instead of numerically. Every observed value of a variable can be referred to as a category. While performing PCA, nonlinear PCA converts every category to a numeric value, in accordance with the variable's analysis level, using optimal quantification. The authors discuss how optimal quantification is carried out, what analysis levels are, which decisions have to be made when applying nonlinear PCA, and how the results can be interpreted. The strengths and limitations of the method are discussed. An example applying nonlinear PCA to empirical data using the program CATPCA (J. J. Meulman, W. J. Heiser, & SPSS, 2004) is provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stability of nonlinear principal components analysis: An empirical study using the balanced bootstrap.

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate normality. For nonlinear PCA, however, standard options for establishing stability are not provided. The authors use the nonparametric bootstrap procedure to assess the stability of nonlinear PCA results, applied to empirical data. They use confidence intervals for the variable transformations and confidence ellipses for the eigenvalues, the component loadings, and the person scores. They discuss the balanced version of the bootstrap, bias estimation, and Procrustes rotation. To provide a benchmark, the same bootstrap procedure is applied to linear PCA on the same data. On the basis of the results, the authors advise using at least 1,000 bootstrap samples, using Procrustes rotation on the bootstrap results, examining the bootstrap distributions along with the confidence regions, and merging categories with small marginal frequencies to reduce the variance of the bootstrap results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

四维空间分形插值算法在品位估算中的应用

克里金法是一种应用广泛的低通滤波性插值方法,但其无法重建原始信息中的高频、低频和局部信息.分形插值算法可利用自相似性,在保留原始信息的基础上,克服克里金插值中低通滤波的局限性,从而提高插值的准确性.本文在传统分形插值算法的基础上,结合地质空间信息,提出了适用于矿床品位估算的四维空间分形插值算法,并将其应用于钼矿的品位估算.结果表明:在该钼矿的品位估算中,四维空间分形插值算法明显优于克里金法.     

The predictive power of ridge regression: Some quasi-simulation results: Correction to Cattin.

Reports an error in the original article by Philippe Cattin (Journal of Applied Psychology, 1981, Vol. 66, No. 3, pp. 282-290). The fifth sentence in the first paragraph on page 284 contains an error. The sentence should read: "For intermediate values of k?, the ridge regression weights are 'weighted sums' of the OLS regression weights [not models] and of the zero-order sample correlations and tend to decrease in absolute value as k? increases." (The following abstract of this article originally appeared in record 1981-27117-001.) Reviews research indicating that ridge regression tends to improve the mean square error of prediction obtained with ordinary least squares (OLS) regression in a wide range of conditions. MMPI profiles of 861 psychiatric patients and diagnoses from 29 psychologists, as collected by P. E. Meehl (see record 1960-04396-001), were used to examine the gains in cross-validated multiple correlation obtained with ridge regression compared with OLS and equal weights as a function of sample size and ridge constant. A simple formula for estimating the ridge constant was also evaluated. Results that are related to recent developments concerning the use of Bayesian regression procedures show that ridge regression improves both the mean square error of prediction and the cross-validated multiple correlation obtained with OLS when the ratio sample size to number of predictor variables is relatively small. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

撰山子金矿REDZL光电测距导线网精度分析

通过对撰山子金矿采用日本测机会生产的REDZL光电测距议所施测的导线网精度系统地分析，提出了用光电测距离程导线代替四等水准测量的方法和措施，从而为不宜布设水准测量的矿区进行高精度高程测量提出了实用、易行的新方法．     

Basic statistics and the inconsistency of multiple comparison procedures.

This paper has two main themes. First, the various statistical measures used in this journal are summarized and their interrelationships described by way of a flow chart. These are the pooled standard deviation, the pooled variance or mean square error (MSE), the standard error of each treatment mean (SEM) and of the difference between two treatment means (SED), and the least difference between two means which is significant at (e.g.) the 5% level of significance (LSD[5%]). The last three measures can be displayed as vertical bars in graphs, and the relationship between the lengths of these bars is graphically illustrated. It is suggested that the LSD is the most useful of these three measures. Second, when the experimenter has no prior hypotheses to be tested using analysis of variance "contrasts," a multiple comparison procedure (MCP) that examines all pair-wise differences between treatment means, may be appropriate. In this paper a fictitious experimental data set is used to compare several well-known MCPs by focussing on a particular operating known characteristic, the consistency of the results between an overall analysis of all treatments and an analysis of a subset of the experimental treatments... (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A principal components analysis of dynamic spatial memory biases.

Research has shown that spatial memory for moving targets is often biased in the direction of implied momentum and implied gravity, suggesting that representations of the subjective experiences of these physical principles contribute to such biases. The present study examined the association between these spatial memory biases. Observers viewed targets that moved horizontally from left to right before disappearing or viewed briefly shown stationary targets. After a target disappeared, observers indicated the vanishing position of the target. Principal components analysis revealed that biases along the horizontal axis of motion loaded on separate components from biases along the vertical axis orthogonal to motion. The findings support the hypothesis that implied momentum and implied gravity biases have unique influences on spatial memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comments on orthogonal components.

Several limitations in the use of an orthogonal components solution, as proposed by Gaito (see 34: 3638), in situations requiring multiple comparisons tests are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于层次分析法和均方差法的矿山通风方案优选

针对老厂锡矿原有通风系统存在的不足,提出了3个具体可行的通风系统方案。在对多个矿山通风方案进行选优的过程中,从经济合理、技术先进可行和安全可靠3方面出发,确定了12个矿井通风系统方案优化指标。综合运用层次分析法和均方差法来得到通风系统方案评判指标的权重,并通过计分法选出最优的通风方案。     

对工程概算调整的一些思考

对调整概算的原因、重点和途径进行了探讨和分析研究，以期达到准确编制工程概算、合理调整概算的目的。