Publications

Detailed Information

Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey

DC Field Value Language
dc.contributor.authorGopaluni, R. Bhushan-
dc.contributor.authorTulsyan, Aditya-
dc.contributor.authorChachuat, Benoit-
dc.contributor.authorHuang, Biao-
dc.contributor.authorLee, Jong Min-
dc.contributor.authorAmjad, Faraz-
dc.contributor.authorDamarla, Seshu Kumar-
dc.contributor.authorKim, Jong Woo-
dc.contributor.authorLawrence, Nathan P.-
dc.date.accessioned2022-10-19T05:16:28Z-
dc.date.available2022-10-19T05:16:28Z-
dc.date.created2022-10-06-
dc.date.issued2020-07-
dc.identifier.citationIFAC-PapersOnLine, Vol.53 No.2, pp.225-236-
dc.identifier.issn2405-8963-
dc.identifier.urihttps://hdl.handle.net/10371/186490-
dc.description.abstractOver the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry. Copyright (C) 2020 The Authors.-
dc.language영어-
dc.publisherIFAC Secretariat-
dc.titleModern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey-
dc.typeArticle-
dc.identifier.doi10.1016/j.ifacol.2020.12.126-
dc.citation.journaltitleIFAC-PapersOnLine-
dc.identifier.wosid000652592500038-
dc.identifier.scopusid2-s2.0-85105089381-
dc.citation.endpage236-
dc.citation.number2-
dc.citation.startpage225-
dc.citation.volume53-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorLee, Jong Min-
dc.type.docTypeProceedings Paper-
dc.description.journalClass1-
Appears in Collections:
Files in This Item:
There are no files associated with this item.

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share