Multi-objective Optimization Based Recursive Feature Elimination for Process Monitoring-Reference-Cited by-同舟云学术

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Multi-objective Optimization Based Recursive Feature Elimination for Process Monitoring

Published:2021-02-03 Issue:2 Volume:53 Page:1081-1099
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Singh Shivendra,Agrawal Anubha,Kodamana Hariprasad,Ramteke Manojkumar^ORCID

Funder

Indian Institute of Technology Delhi

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-021-10430-z.pdf

Reference49 articles.

1. Qin SJ (2003) Statistical process monitoring: basics and beyond. J Chem J Chemomet Soc 17(8–9):480–502

2. Qin SJ (2012) Survey on data-driven industrial process monitoring and diagnosis. Ann Rev Control 36(2):220–234

3. Russell EL, Chiang LH, Braatz RD (2012) Data-driven methods for fault detection and diagnosis in chemical processes. Springer, Berlin

4. Li G, Qin SJ, Zhou D (2010) Geometric properties of partial least squares for process monitoring. Automatica 46(1):204–210

5. Chiang LH, Kotanchek ME, Kordon AK (2004) Fault diagnosis based on fisher discriminant analysis and support vector machines. Comput Chem Eng 28(8):1389–1401

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