区分的ARXモデルの同定におけるデータ分類の改良 Refinement of Data Classification in Piecewise ARX Model Identification

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A piecewise ARX model is a typical model for identification of hybrid dynamical systems. This model consists of several ARX submodels which switch in accordance with the value of the regression vector. There have recently been reported many methods for piecewise ARX model identification based on data classification techniques. This approach first categorizes the observed data into several data sets and then estimates the parameters of the submodels and the switching hyperplanes based on the classification result. However, the result of the data classification procedure contains misclassified data in general, and they may have an adverse effect on the accuracy of the identified model. This paper presents two methods for refining the data classification by re-classifying the candidate for misclassified data based on piecewise linear separability or linear separability of the true data classification on the regression space, respectively. A numerical example also demonstrates the effectiveness of the present methods.

収録刊行物

  • システム制御情報学会論文誌  

    システム制御情報学会論文誌 21(8), 260-268, 2008-08-15 

    一般社団法人 システム制御情報学会

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各種コード

  • NII論文ID(NAID)
    10021837742
  • NII書誌ID(NCID)
    AN1013280X
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    13425668
  • NDL 記事登録ID
    9608061
  • NDL 雑誌分類
    ZM11(科学技術--科学技術一般--制御工学)
  • NDL 請求記号
    Z14-195
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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