1) Kernel principal component analysis
核主元分析法
1.
A nonlinear fault detection method based on kernel principal component analysis (KPCA) is introduced.
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。
2) kernel principal component analysis
核主元分析
1.
Nonlinear process monitoring method based on multi-scale kernel principal component analysis;
基于多尺度核主元分析的非线性过程监控方法研究
2.
Fault diagnosis method based on immune kernel principal component analysis;
基于免疫核主元分析的故障诊断方法
3.
Monitoring model based on kernel principal component analysis and multiple support vector machines and its application
基于核主元分析与多支持向量机的监控诊断方法及其应用
3) KPCA
核主元分析
1.
Predicting the Free Calcium Oxide Content on the Basis of KPCA and Support Vector Machines;
基于核主元分析和支持向量机的f-CaO含量预测
2.
Firstly,basis spaces including the posed ear images and the frontal ear images are obtained using PCA or KPCA.
首先,利用主元分析和核主元分析方法得到姿态人耳图像和正侧面人耳图像的基空间,通过计算两种基空间之间的线性转换关系求出姿态转换矩阵,然后将待测的姿态人耳图像特征集利用基空间姿态转换矩阵转变成正侧面人耳图像特征集,最后用支持向量机进行分类识别。
3.
After the research of support vector machine trained with particle swarm optimization, it integrated with KPCA will be applied in the recognition of face images, utilizing the good performances of support vector machine in study, and we will find a new way for face recogni.
在通过对PSO算法训练支持向量机算法研究后,利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径。
4) kernel principal component analysis(KPCA)
核主元分析
1.
A novel method for palmprint recognition based on kernel principal component analysis(KPCA) and fisher linear discriminant(FLD) is presented.
提出了基于核主元分析(KPCA)和FLD相结合的掌纹识别方法。
2.
The nonlinear components of gait features are extracted based on kernel principal component analysis(KPCA).
首先对每个序列进行运动轮廓抽取,从3个方向(水平、垂直、斜向)对时变的二维轮廓进行扫描,分别转换为对应的一维向量;采用核主元分析法(KPCA)提取步态的非线性特征,在此基础上采用线性支持向量机训练步态分类器组,最后用支持向量机组进行步态识别。
3.
In order to monitor the imperial smelting furnace(ISF)state in time and accurately diagnose the faults,a fault diagnosis approach based on kernel principal component analysis(KPCA)and multi-class classifiers of support vector machine(SVM)was proposed.
为了及时反映密闭鼓风炉冶炼过程状态,实现对密闭鼓风炉炉况的监控与诊断,提出核主元分析和多支持向量机分类的相结合的过程监控与故障诊断方法。
5) kernel PCA
核主元分析
1.
For several complex industry processes,the original fault sources are difficult to identify by using kernel principal component analysis(kernel PCA)methods.
核主元分析(KPCA)在非线性系统的故障检测方面明显优于普通的PCA方法,但存在无法进行故障辨识以及在故障诊断过程常常出现核矩阵K计算困难等难题。
2.
Based on the kernel function, a kind of kernel PCA SVM integrated classifying method through combing the support vector machine with kernel principle component analysis is proposed, and the algorithm realizing steps are presented.
在核函数基础上,提出了一种融合支持向量机和核主元分析的核PCA支持向量机综合集成分类方法,给出了算法实现步骤。
6) principal component analysis(PCA)
主元分析法
1.
To this question,principal component analysis(PCA) method was used to reorganize the factors.
针对这个问题,利用主元分析法将影响因素重组,在此基础上,提出一种基于遗传算法的Elman神经网络模型对铜转炉吹炼终点进行预测。
2.
In addition,the Principal Component Analysis(PCA) for feature tra.
提出了一种融合静态特征与动态特征的步态识别方法,该算法使用小波矩描述步态序列图像的静态特征,接着使用主元分析法对小波矩进行降维,而图像的动态特征则用人体轮廓的3个宽度特征来描述。
3.
Based on the characteristics of the color scanner,the spectral reconstruction of images was studied using the method combining the principal component analysis(PCA) and back-propagation(BP) artificial neural network.
针对彩色扫描仪的特点,采用主元分析法(PCA)和反向传播(BP)人工神经网络(ANN)相结合的方法对图像光谱重构进行研究。
补充资料：容量分析法(见化学分析法)
容量分析法(见化学分析法)
容t分析法见化学分析法
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条