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Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gopaluni, R. Bhushan | - |
dc.contributor.author | Tulsyan, Aditya | - |
dc.contributor.author | Chachuat, Benoit | - |
dc.contributor.author | Huang, Biao | - |
dc.contributor.author | Lee, Jong Min | - |
dc.contributor.author | Amjad, Faraz | - |
dc.contributor.author | Damarla, Seshu Kumar | - |
dc.contributor.author | Kim, Jong Woo | - |
dc.contributor.author | Lawrence, Nathan P. | - |
dc.date.accessioned | 2022-10-19T05:16:28Z | - |
dc.date.available | 2022-10-19T05:16:28Z | - |
dc.date.created | 2022-10-06 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | IFAC-PapersOnLine, Vol.53 No.2, pp.225-236 | - |
dc.identifier.issn | 2405-8963 | - |
dc.identifier.uri | https://hdl.handle.net/10371/186490 | - |
dc.description.abstract | Over 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.language | 영어 | - |
dc.publisher | IFAC Secretariat | - |
dc.title | Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.ifacol.2020.12.126 | - |
dc.citation.journaltitle | IFAC-PapersOnLine | - |
dc.identifier.wosid | 000652592500038 | - |
dc.identifier.scopusid | 2-s2.0-85105089381 | - |
dc.citation.endpage | 236 | - |
dc.citation.number | 2 | - |
dc.citation.startpage | 225 | - |
dc.citation.volume | 53 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lee, Jong Min | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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