文件名称：Kernel-Principal-Component-Analysis-KPCA-master.zip
文件大小：1016KB
文件格式：ZIP
更新时间：2022-08-05 03:35:39
故障诊断 过程监控 KPCA 主元分析
对于某hub上的资源做一些微调，一共有4个demo。demo1: dimensionality reduction or feature extraction demo2: fault detection for a numerical example demo3: fault detection and fault diagnosis for TE process using KPCA demo4: fault detection and fault diagnosis for TE process using Dynamic KPCA(DKPCA)
【文件预览】：
Kernel-Principal-Component-Analysis-KPCA-master
----demo4.m(1KB)
----img()
--------demo3_T2_fd.png(17KB)
--------demo3_T2.png(40KB)
--------demo3_SPE.png(44KB)
--------demo1_2.png(48KB)
--------demo2_SPE.png(35KB)
--------demo3_SPE_fd.png(19KB)
--------demo2_T2.png(37KB)
--------demo1_1.png(55KB)
----refs()
--------Deng_Tian_2011_A new fault isolation method based on unified contribution plots.pdf(123KB)
--------Lee et al_2004_Nonlinear process monitoring using kernel principal component analysis.pdf(420KB)
----data()
--------circledata.mat(15KB)
--------train.mat(79KB)
--------test.mat(150KB)
----func()
--------normalize1.m(1KB)
--------kpca_train.m(3KB)
--------plotCPs.m(842B)
--------comtupeLimit.m(1KB)
--------computeKM.m(470B)
--------CPsKPCA.m(2KB)
--------kpca_test.m(1KB)
--------plotResult.m(983B)
--------computeCPs.m(2KB)
--------constructAM.m(717B)
----demo3.m(1KB)
----demo2.m(787B)
----demo1.m(799B)
----README.md(2KB)
----.gitattributes(66B)