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  • 标题:Dynamic-Inner Partial Least Squares for Dynamic Data Modeling
  • 本地全文:下载
  • 作者:Yining Dong ; S. Joe Qin
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2015
  • 卷号:48
  • 期号:8
  • 页码:117-122
  • DOI:10.1016/j.ifacol.2015.08.167
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractPartial least squares(PLS) regression has been widely used to capture the relationship between inputs and outputs in static system modeling. Several dynamic PLS algorithms were proposed to capture the characteristic of dynamic systems. However, none of these algorithms provides an explicit description for dynamic inner model and outer model. In this paper, a dynamic inner PLS is proposed for dynamic system modelling. The proposed algorithm gives explicit dynamic inner model and makes inner model and outer model consistent at the same time. Several examples are given to show the effectiveness of the proposed algorithm.
  • 关键词:Keywordsdynamic partial least squaresdata-driven modeling
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