主元法

论Jacobi迭代中使用列选主元法

On the Application of Column Pivot Element in Jacobi_Iteration

单纯形最佳主元法的几点重要注记

Some important notes on the best main element simplex method

用类部分主元法解线性规划问题

The Method for Solving the Linear Programming Problem by the Similar Partial Pivot

单纯形最佳主元法的结论欠妥

The Conclusions of " The Method of Optimum Main Term " are Untenable

块对角主元法的并行计算

Parallel Computation of the Block Diagonal Pivoting Method

本文给出块对角主元法的并行计算，证明了用这种方法分解矩阵是稳定的。

In this paper a parallel algorithm of the block diagonal pivoting method is given .

改进主元法在故障检测中的应用

Improved PCA with Application to Fault Detection

文献〔1〕中介绍了求解线性规划问题的单纯形最佳主元法，得到了几个重要结论。

The method of simplex optimum main term in the solution of LP is introduced in the document [ 1 ] , and some important conclusions are drawn .

本文研究了传感器故障数据的重构方法，利用PCA主元分析法进行的数据重构取得了很好的效果；

The reconstruction method to fault data is studied in this paper . The reconstruction with PCA gets excellent outcome .

本文研究的故障诊断算法有：主元分析法（PCA）和神经网络预测法。

This paper focuses on two diagnostic methods for sensor fault : principal component analysis ( PCA ) and artificial neural network predictor .

本人的数值分析课程设计，比较完整！Gauss顺序消去法与Gauss列主元消去法是计算机上常用来求解线性方程组的一种直接的方法。

Gauss law and order eliminate Gauss out PCA elimination method is commonly used computers to solve linear equations in a direct way .

为此，提出了基于主元分析法和FRF的井架损伤识别方法。

Therefore , the derrick damage identification method based on principal component analysis and FRF is put forward .

详细介绍了基于数据的故障诊断方法中的主元分析法（PCA），并分析了其在处理非线性数据时的不足。

Then , this paper discussed the detail of data-based fault diagnosis method . Highlighted principal component analysis ( PCA ), and analyzed the deficiency in dealing with the nonlinear data .

将改进的主元分析法应用于粘菌素发酵过程监测和故障诊断中，仿真结果表明改进的PCA方法避免了Q统计量的保守性，并保证了主元子空间中的信息存量。

The improved PCA is applied to mycetozoan fermentation process monitoring and fault diagnosis . The simulation result shows that the improved PCA can avoid the conservation of Q-statistical test and ensure enough information in principal component subspace .

介绍了一种非线性故障检测方法&核主元分析法（KPCA），通过核函数来完成非线性变换，将变量由非线性的输入空间转换到线性的特征空间。

A nonlinear fault detection method based on kernel principal component analysis ( KPCA ) is introduced . KPCA performs nonlinear transformation by kernel function to map the nonlinear input space into linear feature space .

为提高间歇生产的可重复性，提高批次之间产品的一致性，多向主元分析法（MPCA）广泛应用于间歇生产过程的监控。

In order to reduce the variations of the product quality , multivariate statistical process control methods based on Multi-way Principal Component Analysis ( MPCA ) are used for on-line batch process monitoring .

针对多向主元分析法（MPCA）在间歇过程监控过程中需要预测过程未来输出的困难，提出了一种新的步进多向主元分析方法。

Multi-way principal component analysis ( MPCA ) has been successfully applied to the monitoring of batch and semi-batch process in most chemical industry . A new approach using the process variable trajectories to monitoring batch process was proposed .

基于结构模型和主元分析法的行为识别

Human Activity Recognition Based on Sample Model and PCA

列主元消去法在电磁问题矩量法分析中的应用

Application of Maximal Column Pivot Algorithm to Moment Method Analysis of EM Problems

状态监测与故障诊断中的主元分析法

PCA Approach to Condition Monitoring and Fault Diagnosis

选主元迭代法及其收敛性

Select main element method of iteration and convergence

改进的并行高斯全主元消去法

Improved Parallel Complete Gaussian Pivoting Elimination

在内层迭代中，将顺序消去法和主元消去法相结合，兼顾求解线性方程组的计算速度和计算精度。

In inner iteration , sequence elimination and pivoting elimination are combined to consider both time and precision .

多元统计过程介绍了三种主要的方法：主元分析法、偏最小二乘法和核函数概率密度估计法。

About multivariate statistical process , three methods are introduced : Principal Component Analysis , Partial Least Squares , Kernel Density Estimation .

该方法利用主元分析法可以将多个变量降维解耦的性质，把相互耦合的多变量变成相互独立的低维模型，再对控制系统进行性能评价。

A number of variables can be reduced-order and decoupled by using the PCA approach , and then the independent and low-dimensional variables can be evaluated .

采用主元分析法和径向基神经网络技术建立浮选技术指标预测模型。

The paper adopts principal component analysis ( PCA ) method and radial basis function ( RBF ) neural network technology to build the soft sensors model .

针对这四种典型故障，并利用天津博物馆楼宇控制系统正常状态下的运行数据，分别对基于核主元分析法与主元分析法的故障检测能力进行仿真研究。

Aiming at the four typical faults and using the operating data of Tianjin museum building control system in normal condition , simulations are respectively performed for KPCA and PCA .

主元分析法能够有效地消除超光谱图像的谱间相关性，而整数小波变换在去除空间相关性方面具有长处。

Principal component analysis ( PCA ) can effectively reduce the spectral correlation of hyperspectral image and integer wavelet transform by using lift scheme is widely used for spatial decorrelation .

首先，利用主元分析法初步确定对称轴；然后结合转动惯量法进一步精确定位对称轴直线方程；

First , we used the principal component analysis to determine the initial axis of symmetry , and then combined the moment of inertia to further pinpoint the accurate symmetry-axis linear equation .

文中提出用高斯列主元消去法与最优化方法相结合的技术求解模拟电荷方程组，是提高精度和保证收敛的一种有效算法。

The combined method of gnass elimination and optimization is used to solve the equation of charge-simulation , and it is an effective method for increasing the accuracy and assuring the convergence .