パルスニューラルネットワークのための学習率最適化を用いた誤差逆伝播学習法 Fast Back Propagation Learning Using Optimization of Learning Rate for Pulsed Neural Networks

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Neural Networks (NN) are widely applied to information processing because of its nonlinear processing capability. Digital hardware implementation of NN seems to be effective in construction of NN systems in which real-time operation and much further wide applications are possible. However, the digital hardware implementation of analogue NN is very difficult because we have to fulfill the restrictions about circuit resource, such as circuit scale, arrangement, and wiring. A technique that uses pulsed neuron model instead of analogue neuron model as a method of solving this problem has been proposed, and its effectiveness has been confirmed. To construct Pulsed Neural Networks (PNN), Back Propagation (BP) learning has been proposed. However, BP learning takes much time to construct PNN compared with the learning of analogue NN. Therefore some method to speed up BP learning of PNN is necessary. In this paper, we propose a fast BP learning using optimization of learning rate for PNN. In the proposed method, the learning rate is optimized so as to speed up the learning at every learning epoch. To evaluate the proposed method, we apply it to some pattern recognition problems, such as XOR, 3-bits parity, and digit recognition. Results of computational experiments indicate the validity of 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 128(7), 1137-1142, 2008-07-01 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10021133154
  • NII書誌ID(NCID)
    AN10065950
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    03854221
  • NDL 記事登録ID
    9564387
  • 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  CJP引用  NDL  J-STAGE 
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