Nonlinear Time Series Prediction Using Wavelet Network with Kalman Filter Based Algorithm

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The idea of combining both wavelets and neural networks has resulted in the formulation of wavelet network, whose basic functions are drawn from a family of orthonormal wavelets<sup>(1)</sup>, which absorbs the advantage of high resolution of wavelets and the advantages of learning and feedforward of neural networks. The usual method to train wavelet networks is the backpropagation (BP) algorithm described by Rumelhart et al. However, this algorithm converges slowly for large or complex problems. In this paper, we propose to train wavelet network for nonlinear time series prediction by using the Unscented Kalman filter (UKF), which outperforms the conventional BP method and several other reference methods. Several simulation results are presented to validate the proposed method.

収録刊行物

  • 電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society  

    電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society 126(10), 1255-1260, 2006-10-01 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10018780748
  • NII書誌ID(NCID)
    AN10065950
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    03854221
  • NDL 記事登録ID
    8526321
  • NDL 雑誌分類
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL 請求記号
    Z16-795
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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