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  • 标题:A Bias-Correction Approach for the Identification of Piecewise Affine Output-Error Models ⁎
  • 本地全文:下载
  • 作者:Manas Mejari ; Valentina Breschi ; Vihangkumar V. Naik
  • 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:1096-1101
  • DOI:10.1016/j.ifacol.2020.12.1307
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe paper presents an algorithm for the identification ofPieceWise Affine Output-Error(PWA-OE) models, which involves the estimation of the parameters defining affine submodels as well as a partition of the regressor space. For the estimation of affine submodel parameters, a bias-correction scheme is presented to correct the bias in the least squares estimates which is caused by the output-error noise structure. The obtained bias-corrected estimates are proven to be consistent under suitable assumptions. The bias-correction method is then combined with a recursive estimation algorithm for clustering the regressors. These clusters are used to compute a partition of the regressor space by employing linear multi-category discrimination. The effectiveness of the proposed methodology is demonstrated via a simulation case study.
  • 关键词:KeywordsHybrid systemsPWA regressionbias corrected least-squaresoutput-error models
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