提出一种基于核主元分析(KPCA)和多级神经网络集成的汽轮机故障诊断方法。
One new method for fault diagnosis of steam turbine based on kernel principal component analysis (KPCA) and multistage neural network ensemble was proposed.
为此提出了一种多向核主元分析(MKPCA)算法用于间歇过程的建模与在线监测。
A method based on multiway kernel principal component analysis (MKPCA) was proposed to capture the nonlinear characteristics of normal batch processes.
结合核主元分析与支持向量机的特点，提出了一种基于核主元分析与支持向量机的人脸识别方法。
By integrating the characteristics of KPCA and SVM, a face recognition method based on these two algorithms is presented.
在具体分析了多种建模方法的基础上，提出了核主元分析结合最小二乘支持向量机软测量建模方法。
On the basis of analysis of several methods for modeling, a soft sensor based on kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed.
该方法首先利用核主元分析对人脸图像进行特征提取，然后依据支持向量机与最近邻准则对所提取的核主元特征进行分类识别。
Firstly KPCA is used to extract the features of human face image, and then SVM combined with the nearest distance rule is used for classification, which depends on the kernel principal components.
主要内容如下：对基于主元分析的方法进行了综合的研究：从故障检测、故障诊断、故障重构以及基于核主元分析的故障检测方法。
Completed work is summarized as following: The paper gives a integrated research based on PCA from fault detection, fault diagnosis, reconstruction fault to a new fault detection method based on KPCA.
建立以Fisher判别函数为优化目标的适应度，利用粒子群算法中多个随机粒子实现核函数参数的优化，改善了核主元分析方法的性能。
Firstly, it constructs a fitness function which Fisher discriminate function is optimized object, then WCPSO is used to optimize it by its many random particles to improve the performance of KPCA.
在训练阶段，核-主元分析用来捕捉非线性的手写变化。
In the training phase, kernel principal component analysis is used to capture nonlinear handwriting variations.
在训练阶段，核-主元分析用来捕捉非线性的手写变化。
The nonlinear components of gait features are extracted based on kernel principal component analysis (KPCA).
提出了基于核函数主元分析的齿轮故障诊断方法。
An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA).
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.
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.
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