脑卒中发病环境因素的定量分析模型

利用提供的数据,在合理假设前提下,给出了脑卒中发病环境因素分析模型．首先采用主成分分析法建立模型,得出影响脑卒中发病的三个因素中,第一类主成分是气温和气压,第二类主成分是相对湿度．其次利用回归分析法得到回归方程,并利用部分数据进行检验．     

主成分在企业成长性分析中的应用

利用主成分分析方法对中小企业版上市公司的成长性进行评价和分析,并建立相应的评价指标体系,结果表明主成分分析方法评估的合理性,然后根据主成分分析方法本身的缺陷提出了一种酶进模型,采用等级相关方法进行预处理后再作主成分分析,并建立综合评价的指标体系,研究结果表明评估比较合理,能够客观上反映股票的成长性。     

基于主成分分析法对乳制品营养成分分析

乳制品与人们的日常生活息息相关,乳制品的价格各不相同,不同的价格背后所反映出的是营养成分的不同,为了探讨营养成分对乳制品价格的影响,本文在对乳制品进行调查统计的基础上运用主成分分析对乳制品的特征进行提取,实现了主成分分析用于乳制品营养成分定量分析的建模方法.结果表明:乳制品的营养成分特征性指标主要为蛋白质、碳水化合物、脂肪、钠、钙、能量组成;能量和钙则是这些营养成分里面对价格的影响较大的因子.此结论基本上是合理的,对于消费者选购合适的乳制品具有一定的指导作用,可以帮助消费者更为理想地消费乳制品.     

Literacy components model for at‐risk young adults enrolled in career and technical education

Little is known about literacy skills of an important adult population: educationally and economically disadvantaged young adults. This exploratory study used principal components analysis of 33 measures of reading subskills, spelling, maths, memory and other cognitive processes among 335 at‐risk 16‐ to 24‐year‐olds enrolled in a career and technical education programme. The analysis yielded a five‐component model explaining 69% of the variance in the data. Most of this variance was attributed to two components: element and word skills (including spelling) and language comprehension (including vocabulary). Using these components, models predicting two performance criterion measures yielded different results. A model using all five components predicted 51% of functional reading measured by the Comprehensive Adult Student Assessment System. A three‐component model with element and word skills, language comprehension and processing speed predicted 55% of variance in reading comprehension measured by the Test of Adult Basic Education. Implications of these findings for further research are discussed.     

Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir’s Outflow

In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October.     

主成分分析在综合评价中的应用

应用主成分分析原理,以少数的综合变量取代原有的多维变量,使数据结构简化,把原指标综合成几个主成分,再以这几个主成分的贡献率为权数进行加权平均,构造出一个综合评价函数.文章应用该原理对23家电器企业的实力进行了综合评价,其结果是合理可信的.     

陆地棉F_2代单株选择的主成分分析

以抗虫棉品种和非抗虫棉杂交,在选择的条件下对F2代单株性状采用主成分分析的方法,就综合农艺性状对产量性状的影响进行了研究.结果表明：前5个主成分因子累积方差贡献率达74.95%,基本反映遗传信息.各主成分分析结果依次为：结铃性因子、形态因子、铃重因子、衣分因子、营养生长因子.依据主成分评价,较为理想的杂交F2代抗虫棉单株应是第1主成分较大,第2主成分适中的品种,即以果节数、果枝数和结铃数多,果枝夹角、株高和铃重适中的类型.通过5个主成分的总得分,构建出新的一个F2代单株选择指数,可以此作为F2代单株选择的依据.     

Population Proportion of Explained Variance in Principal Component Analysis: A Note on Its Evaluation Via a Large-Sample Approach

A large-sample procedure for examining explained population variance in principal component analysis is discussed. The method yields interval estimates for the proportions of variance accounted for by the considered for retention principal components. The obtained confidence intervals can be used to make decisions about the number of selected components, based on their performance in studied populations. The approach can also be employed for ascertaining group similarity or differences in percentage explained variance by the considered principal components. The procedure is illustrated with 2 examples.     

基于全寿命周期理论的工程项目健康检测体系构建

为了更有效地对工程项目的健康状态做出全面准确的评估,将全寿命期下工程项目所有监测指标按质量指标、费用指标、时间指标、各方面满意度和可持续发展指标等加以划分．并且基于该指标体系定性与定量指标相结合的特征,构建了PCA—PR分析模型．该模型先对全寿命期指标体系进行主成分分析（PCA）,从之前构建的全寿命期指标中甄选出一批可以作为工程项目健康检测分析的主要特征指标体系;再针对这些主要特征指标体系进行模式识别分析（PR）,即通过将工程项目即时可能状态划分为绿灯至红灯5种状态,利用模式识别模型和项目主要特征指标识别出项目任意时点的健康状态．最后结合实例进行相关分析,得出与实际情况较为吻合的分析结果,验证了该指标体系和模型的有效性．     

Batch process monitoring based on multilevel ICA-PCA

In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis （MLICA-PCA）. Unlike the conventional multi-way principal component analysis （MPCA） method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels： the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted.I^2 T^2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.     

新疆各地州市区域经济发展的多元统计分析

本文结合新疆实际,选取了9项适当的主要经济指标对新疆15个地州区域经济发展水平进行了聚类分析和主成份分析。先确立指标体系,用聚类分析法宏观地将新疆15个地州市进行分类,划分的两类具体为：乌鲁木齐、克拉玛依为一类;其余13个地州为一类。接着用主成份分析法微观地对新疆各地州经济发展水平进行排名,最后对结果进行分析。     

城市需水预测算法比较

建立高精度水量预测算法模型,有利于水资源充分利用。以北京市2002-2015年需水量为例,对数据进行相关性分析后选出主要影响因素,然后采用主成分回归法、逐步回归法、灰色模型以及BP神经网络共4种方法进行建模,并用北京市2016年和2017年数据进行模型精度验证。结果表明：4种方法都适合用于城市需水量预测,其中主成分分析和逐步回归分析两种方法主要考虑了多元线性回归存在多重共线性,但是逐步回归模型优于主成分回归模型。将4种模型进行对比验证,BP神经网络模型预测精度最高,平均相对误差达到0.79%,用来预测2016-2017年需水量,预测结果分别为38.66亿m3、39.49亿m3,适合作为城市需水量预测方法。     

基于主成分分析的汶川地震灾情的综合评价

利用主成分分析运用SPSS软件对汶川地震36个严重受灾县市进行了综合分析.一方面,得到了用来判断灾情严重程度的主要灾情指标,另一方面,依据主成分的综合得分对36个县市按照灾情严重程度进行了排序.同时,依据主成分综合得分的均值化数值对36个县市进行了分类.     

一种新的BP神经网络人脸识别方法研究

BP神经网络在人脸识别方面的研究中,原始样本数据不进行预处理与特征提取,不仅使识别结果准确降低,而且使BP神经的结构复杂化.主成分分析法能提取代替样本的少数几个主成分,这些主成分彼此不相关,符合特征优化的要求.在BioID人脸数据库实验表明,将主成分分析与BP神经网络相结合,与传统单一的BP神经网络识别相比,能够提高识别的正确率,减少训练时间,同时简化网络结构,减少很大的计算量.     

基于PCA和ANFIS的黄土湿陷系数预测

运用人工智能原理,提出一种基于主成分分析法（PCA）与自适应神经模糊推理系统（ANFIS）的黄土湿陷系数预测方法．首先通过主成分分析对黄土的物理指标提取主成分,以消除变量间的相关性和减少模型输入量的目的;再利用神经网络的高自适应性和模糊推理系统的推理能力建立ANFIS模型,提出一种新的黄土湿陷性预测方法．通过实测数据和预测数据的对比分析,平均误差0．29％,最大误差20％,在工程上可以接受的范围,实例说明这种预测方法是可行的．     

网上主成分分析可视化设计

利Microsoft Visual J＋＋6．0作为实验开发平台,采用Java语言及Applet嵌入网页技术,实现基于互联网上主成分分析过程的可视化,用户可通过实例演示该实验设计的各功能：用户对相关原理的学习,数据的录入、修改,显示主成分个数及相应的累计贡献率.     

非均匀稳态环境局部热感觉对整体热感觉的影响

为了揭示人体热反应机理、分析局部热感觉对整体热感觉的影响,通过对身体7个不同部位的冷刺激,开展了对人体主观热反应的实验研究（取94个样本）,并建立了整体热感觉预测模型.为消除各部位间存在的多重共线性影响,采用主成分回归方法获得不同部位刺激工况下的主成分.然后通过多元线性回归和主成分分析,得到7个部位的权重系数.最后,基于此权重系数建立整体热感觉的预测模型.分析结果表明,躯干和肢体末端表现出不同的热反应特性,局部对整体热感觉的权重系数在物理意义上为一综合反应量,并且不同部位的权重系数相差不大.     

Principal component analysis using LISREL 8

The object of this article is to demonstrate that the widely disseminated program LISREL 8 can be used to carry out principal component analyses. LISREL 8 offers a number of useful possibilities including multigroup principal component analysis and principal component regression. Several illustrative analyses are reported.     

基于灰色预测与BP神经网络的全球温度预测研究

为深入了解全球变暖缘由及影响,探究变暖是否停滞,考虑地球吸热、散热及海洋温度变化等因素,构建一种全球温度预测模型,预测未来25年温度变化。采用主成分分析法找出贡献度较大的3个主成分,再用3组灰色预测模型预测海洋平均温度、二氧化碳排放量、太阳长波辐射等8个变量,并进行光滑比、级比和残差检验。结果表明,预测符合前29年（1990-2018年）时间序列图规律,用历史数据训练BP神经网络,然后把8个变量的预测值代入神经网络,拟合优度为0.922 72,精度非常高,可以看出全球平均温度距平序列越来越大,说明温度正逐渐升高,而不是停滞。     

Development of a scale on learners' views on blended learning and its implementation process

The purpose of this study was to extend the evaluation of learners' views on blended learning and its implementation process by developing and validating an objective assessment instrument. Cronbach' alpha, item analysis and item discrimination indices, principal component analysis, varimax rotation, and discriminant validity were used to measure the reliability and validity of the scale. A 50-item refined version of the scale was found to be highly reliable and of reasonable length.Further refinement was made based on principal component analysis. This indicated two major components, which facilitate the elicitation of learners' views on blended learning and its implementation process in relation to the complexity levels of the learning process.The scale is recommended to identify individual's views on blended learning and its implementation process and thus, to contribute to the improvement of a blended learning environment for learners.