カオスニューラルネットワーク連想記憶モデルにおける活性化関数の形状とその評価 Shapes of Non-monotonous Activation Functions in Chaotic Neural Network Associative Memory Model and Its Evaluation

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The purpose of this paper is to investigate the performance of the associative memory model using Aihara's chaotic neural network with different activation functions. Sigmoid function, a monotonous function, was used in Aihara's original model. However, in the static associative memory, it is reported that the storage capacity of the network is improved when a non-monotonous function is used as the activation function. To improve the associative ability of chaotic neural network, kinds of non-monotonous functions have been proposed to serve as activation function. This paper investigates their difference as to retrieval ability, and proposes an advanced non-monotonous function. By computer simulation, we discuss about what kind of shape is good to improve the associative ability of chaotic neural network.

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(11), 1401-1405, 2006-11-01 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10018318461
  • 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
    8560480
  • 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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