A hybrid ICA-SVM approach for determining the quality variables at fault in a multivariate process

DOI10.1155/2012/284910zbMath1264.94066OpenAlexW2015157547WikidataQ58911382 ScholiaQ58911382MaRDI QIDQ1954609

Yu-Chiun Wang, Yuehjen E. Shao, Chi-Jie Lu

Publication date: 11 June 2013

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/284910

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (1)

Hybrid artificial neural networks modeling for faults identification of a stochastic multivariate process

Uses Software

• LIBSVM
• FastICA

Cites Work

• Unnamed Item
• Frontiers of research in BSS/ICA.
• Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis
• Contributors to a multivariate statistical process control chart signal
• Integration of support vector machines and control charts for multivariate process monitoring
• Integrated Application of SPC/EPC/ICA and neural networks

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