.png){kind=icon}
返回
Nonlinear feature extraction for soft sensor modeling based on weighted probabilistic PCA
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2015.08.014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As industrial process plants are often instrumented with a large number of sensors, it is important to carry out feature extraction before soft sensor modeling. Probabilistic principal component analysis (PPCA) has been identified as an effective method for dimensional reduction. However, PPCA is a linear method, which cannot deal with nonlinear data distribution. To cope with this problem and enhance the performance of soft sensor model, a new nonlinear dimensional reduction method, weighted probabilistic principal component analysis (WPPCA), is proposed in this paper. By assigning different weights for training samples according to their similarities to the testing sample, nonlinear features can be extracted properly for regression modeling. For performance evaluation of the proposed method, detailed illustrations of a numerical example and an industrial process are provided. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Soft sensor
Principal component analysis (PCA)
Probabilistic principal component analysis (PPCA)
Weighted probabilistic principal component analysis (WPPCA)
Quality prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
4.6K
被引数:
1.2W
机构
引用论文
Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression基于高斯混合回归的多相/多模过程软测量模型开发
.png){kind=icon}
PREAI
分享收藏A Scanning Electron Microscope Study of Etched Aluminum Foil for Electrolytic Capacitors电解电容器用腐蚀铝箔的扫描电子显微镜研究
PREAI
分享收藏分享收藏
查看更多内容