在多变量统计过程控制中，传统的方法主要包括主元分析和偏最小二乘，这些方法存在着诸多缺陷。
Principal component analysis (PCA) and partial least squares (PLS) are the conventional techniques of multivariate statistical process control but exist some defects.
引入了主成分分析与偏最小二乘回归等多元统计与回归方法，并分析了其基本思想与优缺点。
Then the basic concepts of principal component analysis and partial least square regression are studied, as well as their advantages and disadvantages.
本文提出采用因素分析方法，对偏最小二乘回归的最优子空间进行正交变换。
In this paper the factor analysis method is presented to transform orthogonally the optimal subspace, which is obtained from partial least squares regression.
介绍了多变量统计投影方法的主要理论基础，包括：主元分析(PCA)、主元回归(PCR)、偏最小二乘(PLS)。
The paper briefly introduced the theoretical foundation of MSP method, which include Principle Component Analysis (PCA), Principle Component Regression (PCR), and Partial Least Squares (PLS).
采用统计分析和偏最小二乘回归方法提出了过程稳定性在线评价模型，克服了输入变量严重多重相关性的问题。
Based on this, an on-line evaluation model of process stability with statistical method and partial-least-square regression (PLSR) was set up which overcome the multicollinearity of input parameters.
证明出，偏最小二乘迭代算法在处理单张数据表成分提取时与主成分分析相同。
Obtained: Partial Least Squares iterative algorithm to handle data sheet leaflet extraction equivalence with Principal Component Analysis.
本文利用偏最小二乘回归分析方法和四分模型找出影响顾客满意度的关键因素。
This paper use partial least squares regression analysis and quarter of the model to identify the key factors affecting customer satisfaction.
本文利用偏最小二乘回归分析方法和四分模型找出影响顾客满意度的关键因素。
This paper use partial least squares regression analysis and quarter of the model to identify the key factors affecting customer satisfaction.
应用推荐