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  • 标题:Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
  • 本地全文:下载
  • 作者:R. Bhushan Gopaluni ; Aditya Tulsyan ; Benoit Chachuat
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:218-229
  • DOI:10.1016/j.ifacol.2020.12.126
  • 语种:English
  • 出版社:Elsevier
  • 摘要: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.
  • 关键词:Keywordsstatistical machine learningdeep learningreinforcement learningmonitoringcontrol
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