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).
它具有比主元分析更好的刻画过程运行特征的性能。
So ICA is more effective than PCA (Principal Component Analysis) in describing process.
文中介绍了主元分析算法以及在故障检测方面的应用。
In this paper, the principle component analysis (PCA) theory is introduced, and the theory is used for fault diagnosis of lock of actuator.
讨论了基于多尺度主元分析的故障传感器数据重构问题。
Multi-Scale Principal Component Analysis for data reconstruction of the faulty sensor is discussed.
内容涉及模糊分割技术、BP神经网络、主元分析技术。
The content includes in the fuzzy segment, BP neural networks and principal component analysis.
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).
详细介绍了主元分析方法、其存在的缺点及相应的改善方法。
Principal Component Analysis (PCA) is studied and some modifications are discussed to improve the performance of PCA.
基于主元分析(pca)的人脸特征提取MATLAB实现。
Based on principal component analysis (pca) Face Feature Extraction MATLAB implementation.
其次，着重研究了基于主元分析的生产过程异常检测与诊断系方法。
Secondly, the developing method of fault detection and diagnosis system based on PCA for the production process is discussed.
对于连续生产过程，可以采用普通的主元分析方法进行过程数据分析；
For continuous process, the normal PCA can be used to analyze the process data.
研究了基于高阶谱（主要是三阶谱）和主元分析的故障特征提取方法。
The extracting fault features approach based on high-order spectral for pump valves of reciprocating pump was investigated, especially third order spectral analysis.
根据现实过程测量数据的时序相关特点，提出了一种动态主元分析方法；
To meet the needs of timely responses of process parameter measurements on paper machine operations, an analytical method based on the major dynamic components is applied on a paper process model.
尝试性地将基于主元分析的多变量统计方法应用于连铸结晶器过程的监测。
The authors try to use multivariate statistics method based on principal component analysis (PCA) to monitor continuous casting mould process.
该方法可辨识系统中相关性较高的若干传感器，并为之建立主元分析模型。
The main element analysis model was established with the several sensors highly related in the identification system of the diagnosis method.
提出一种基于人脸肤色统计模型和主元分析(pca)的人脸检测和定位方法。
This paper presents a human face detection and localization approach which is based on skin color detection and principle component analysis (PCA).
基于此，提出基于主元分析(pca)和改进K均值相结合的动作电位分类方法。
This paper proposes a method based on Principal Component Analysis (PCA) combined with improved K-means for action potential classification.
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.
它同时克服了对传感器测量的数据直接进行主元分析需要诊断的传感器较多的不足。
It overcomes the defect of demanding more sensors when testing the data of sensors. A simulation experiment testifies to the effectiveness of this method.
实验结果表明，基于主元分析方法的图像序列融合能更好地提取木板表面缺陷特征。
The emulated resups show that more distinct features can be extracted from the four images of a same surface by fusing the image series with PCA.
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.
对多元统计过程控制常用的分析方法进行了介绍和总结，如主元分析、多向主元分析。
Conventional analytical methods used in Multivariate Statistical Process Control, such as Principal Component Analysis, Multi-way Principal Component Analysis are summarized.
该方法由三部分组成：主元分析pca、时间延迟神经网络、软测量模型的在线校正。
It is composed of three elements: PCA, time-delay neural network and model updating, where the offline model is trained through the algorithm GABP.
然而，传统主元分析直接应用于工业过程时，由于其本身性能的限制，会出现很多问题。
However, when applying conventional PCA to industrial process monitoring, a lot of problems appear because of its performance limit.
应用基于主元分析的故障诊断方法对浮式油轮生产储油卸油系统进行故障检测与诊断研究。
Used fault diagnosis method based on Principal Components Analysis to research the fault monitoring and diagnosis of Floating Production Storage and Off-loading system.
基于主元分析(pca)的统计检测方法已经被广泛应用于各种化工过程的故障检测和识别。
Numerous statistical process monitoring methods based on principal component analysis (PCA) have been developed and applied to various chemical processes for fault detection and identification.
应用主元分析方法将高维数据转换到低维数据空间，这使得过程监测可以在低维的空间内进行。
High dimension was changed into low dimension by using principal component analysis method, process detecting could be carried out in the low dimension space.
概率主元分析(PPCA)能够根据过程变量的预测误差及其主元的白化值实现对过程的监控。
Probabilistic principal component analysis (PPCA) can realize the process monitoring (according) to the whiten values of process variables' prediction error and their scores.
提出利用主元分析（PCA）和学习矢量量化神经网络（LVQ）相结合的方法进行人脸识别。
This paper proposes a face recognition method based on PCA and LVQ neural networks.
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.
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