共查询到20条相似文献，搜索用时 203 毫秒
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职业院校学生自我认知满意度评价模型研究 总被引：1，自引：0，他引：1
张光照 《中国职业技术教育》2012,(2):74-79
本文从职业院校学生自我认知的满意度模型参数估计方法应用情况入手,结合实例,分析了常用的传统回归方法的应用与主成分回归方法的优势互补方案,同时,简单地介绍了目前技术已经比较成熟的结构方程模型和偏最小二乘法的基本原理及其应用,并通过对几种参数估计方法的对比研究。指出在满意度研究过程中,应将多种参数估计方法结合起来使用,才能收到更好的效果。 相似文献
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周鑫 《数学学习与研究(教研版)》2015,(3):127
文章介绍了处理多元线性回归模型中多重共线性问题的有偏回归方法——岭回归和偏最小二乘回归,并通过实例比较了两种方法建立的回归方程的拟合效果,而偏最小二乘回归方法相对岭回归方法要更优. 相似文献
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司圣柱 《安徽教育学院学报》2006,24(3):50-53
将偏最小二乘法(PLS)与可见分光光度法相结合,对胭脂红、苋菜红、日落黄三种食用色素进行不经分离的同时定量分析。使用交叉验证法选择主成分数建立了校正模型,预测结果令人满意。采用随机抽样方法研究了校正模型中样品集和样本容量对预测能力的影响,结果表明,在文章所研究的体系中,偏最小二乘法的预测能力不因样品集和样本容量的不同而有明显差异,从而说明了文章所用方法具有较大的可靠性和适用性。 相似文献
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实验尝试采用近红外光谱法结合涡旋辅助液液微萃取技术,快速测定水中邻苯二甲酸二（2-乙基己基）酯（DEHP）残留.为提高检测的信噪比,使用四氯化碳作为萃取剂,不使用分散剂.近红外光谱数据采用偏最小二乘法分析,利用auto-scale对光谱进行预处理.偏最小二乘分析模型的主因子数为4,建模波段为4246.7～4424.1 cm-1.模型中RMSECV为0.3759,R2为0.9607.本实验结果显示了近红外方法结合涡旋辅助液液微萃取在分析环境水中的DEHP方面具有潜力. 相似文献
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针对变压器油击穿电压在线测量困难,提出核主元分析(KPCA)和模糊C均值聚类(FCM)的变压器油击穿电压预测模型。首先,通过KPCA提取输人数据的非线性主元;然后采用FCM将提取的主元集分成具有不同聚类中心的子集,同时,采用差分进化算法对KPCA核参数和FCM聚类数寻优,分别为每一子集建立最小二乘支持向量机(LSSVM)子模型;最后通过子模型切换策略得到模型的最终输出。实验结果表明,提出的预测模型具有较好的泛化能力和预测精度。 相似文献
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胡煜 《广东技术师范学院学报》2008,(6)
本文主要采用两种降维的方法和k-近邻法(KNN)有监督分类的方法来对基因芯片(微阵列)数据进行分析。PCA,PLS是一种提取海量的数据有效特征的有效方法,可以获得与原来基因芯片数据更为接近的成分的提取特征的效果。比较PCA降维方法和PLS降维方法对KNN统计判别分类的效果。 相似文献
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胡煜 《广东技术师范学院学报》2007,(10):25-27,24
本文主要采用主分量分析方法和二次判别分析(QDA)有监督分类的方法来对基因芯片(微阵列)数据进行分析.PCA是一种提取海量的数据有效特征的有效方法.可以获得与原来基因芯片数据更为接近的成分的提取特征的效果.实验表明采用PCA方法事先对数据处理不可以提高基因芯片数据分析的准确性.得出结论可为工业应用提供科学依据. 相似文献
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Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems. 相似文献
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综述了多工序制造过程质量偏差流的主要研究方法,即状态空间建模方法和统计过程控制方法,评述了两种研究方法的建模思想、在过程监测和故障诊断方面的应用以及局限性。 相似文献
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W. Holmes Finch Maria E. Hernández Finch Brooke Avery 《Learning disabilities research & practice》2023,38(2):104-118
Progress monitoring using curriculum-based measures administered to a student at multiple points in time is common in educational settings. Recent research has demonstrated that common approaches to identifying individuals in need of special services, such as the trend line or median techniques, can be negatively impacted by the nonlinear change in scores over time. The purpose of this study was to test and demonstrate a nonlinear regression model for adjusting the linear trend line for the presence of such nonlinearities, thereby improving the accuracy of common methods for identifying students in need of special services. Results demonstrated that use of this nonlinear model improved the accuracy of common methods for identifying students in need of special services. 相似文献
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M. Hafidz Omar 《Journal of Educational Measurement》2010,47(1):18-35
Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation charts are introduced as techniques for ensuring quality in a measurement process for rating performance items in operational assessments. Several strengths and weaknesses of the procedures are explored with illustrative real and simulated rating data. Further research directions are also suggested . 相似文献
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A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) framework is proposed
to account for plant model errors caused by slow aging, drift in operational conditions, or environmental changes. Since PLS
decomposition structure enables multi-loop controller design within latent spaces, a multivariable adaptive control scheme
can be converted easily into several independent univariable control loops in the PLS space. In each latent subspace, once
the model error exceeds a specific threshold, online adaptation rules are implemented separately to correct the plant model
mismatch via a recursive least squares (RLS) algorithm. Because the IMC extracts the inverse of the minimum part of the internal
model as its structure, the IMC controller is self-tuned by explicitly updating the parameters, which are parts of the internal
model. Both parameter convergence and system stability are briefly analyzed, and proved to be effective. Finally, the proposed
control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output (MIMO) system with pure
delay. 相似文献
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The water distribution system of one residential district in Tianjin is taken as an example to analyze the changes of water quality. Partial least squares (PLS) regression model, in which the turbidity and Fe are regarded as con-trol objectives, is used to establish the statistical model. The experimental results indicate that the PLS regression model has good predicted results of water quality compared with the monitored data. The percentages of absolute relative error (below 15%, 20%, 30%) are 44.4%, 66.7%, 100% (turbidity) and 33.3%, 44.4%, 77.8% (Fe) on the 4th sampling point; 77.8%, 88.9%, 88.9% (turbidity) and 44.4%, 55.6%, 66.7% (Fe) on the 5th sampling point. 相似文献
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Keith F. Widaman 《Structural equation modeling》2018,25(6):829-847
Common factor analysis (FA) and principal component analysis (PCA) are commonly used to obtain lower-dimensional representations of matrices of correlations among manifest variables. Whereas some experts argue that differences in results from use of FA and PCA are small and relatively unimportant in empirical studies, the fundamental rationales for the two methods are very different. Here, FA and PCA are contrasted on four key issues: the range of possible dimensional loadings, the range of potential correlations among dimensions, the structure of residual covariances and correlations, and the relation between population parameters and the correlational structures with which they are associated. For decades, experts have emphasized indeterminacies of the FA model, particularly indeterminacy of common factor scores. Determinate in most respects, a heretofore unacknowledged, pernicious indeterminacy of PCA is demonstrated: the indeterminacy between PCA structural representations and the correlational structures from which they are derived. Researchers are often advised to use either FA or PCA in exploratory rounds of data analysis to understand and refine the dimensional structure of a domain before moving to Structural Equation Modeling in later theory-testing, confirmatory, replication studies. Results from the current study suggest that PCA is an unreliable method to use for such purposes and may lead to serious misrepresentation of the structure of a domain. Hence, PCA should never be used if the goal is to understand and represent the latent structure of a domain; only FA techniques should be used for this purpose, as only FA provides reliable structural representations as the basis for confirmatory tests in future studies. 相似文献