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  • 标题:Input variable selection for an inferential predictor using the retrospective Taguchi method
  • 本地全文:下载
  • 作者:M. Musfiqur Rahman ; Syed Ahmad Imtiaz ; Kelly Hawboldt
  • 期刊名称:Canadian Journal of Chemical Engineering
  • 印刷版ISSN:0008-4034
  • 出版年度:2015
  • 卷号:93
  • 期号:10
  • 页码:1760-1769
  • DOI:10.1002/cjce.22222
  • 语种:English
  • 出版社:Chemical Institute of Canada
  • 摘要:Abstract A systematic method based on Taguchi's experimental design approach is proposed for selecting input variables for an inferential predictor. Several implementation difficulties arising from dynamic variation and correlation among process variables are addressed. The predictor is developed using support vector regression (SVR) in order to capture the nonlinearity in the process. The prediction performance of the proposed Taguchi‐SVR is compared with the existing variable importance in projection (VIP)'SVR method. The industrial case study clearly indicates that the proposed methodology can be a valuable tool for process variable selection and it can improve the prediction performance of the inferential predictor.
  • 关键词:variable selectionretrospective Taguchisupport vector regressioninferential predictor
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