Compound dimensionality reduction based multi-dynamic kernel principal component analysis monitoring method for batch process with large-scale data sets-Reference-Cited by-同舟云学术

Compound dimensionality reduction based multi-dynamic kernel principal component analysis monitoring method for batch process with large-scale data sets

Published:2020-01-09 Issue:1 Volume:38 Page:471-480
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Wang Yajun¹,Sun Fuming²,Li Xiaohui¹

Affiliation:

1. School of Eclectronics and Information Engineering, Liaoning University of Technology, Jinzhou, China

2. School of Information and Communication Engineering, Dalian Minzu University, Dalian, China

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference25 articles.

1. A fast monitoring method for multiple operating batch processes with incomplete modeling data types;Wang;J Ind Eng Chem,2015

2. Pseudo time slice construction using variable moving window-k nearest neighbor (VMW-kNN) rule for sequential uneven phase division and batch process monitoring;Zhang;Ind Eng Chem Re,2016

3. Multiscale neighborhood normalization-based multiple dynamic PCA monitoring method for batch processes with frequent operations;Wang;IEEE Trans Autom. Sci Eng,2018

4. Non-Gaussian industrial process monitoring with probabilistic independent component analysis;Zhu;IEEE Trans Autom Sci Eng,2016

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