Periodic Chaos Neural Network with Autocorrelation Dynamics

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概要

• 論文の詳細を見る

• In this report we shall propose a novel chaos neural network model applied to memory search and the autoassociation. The present artificial neuron model is substantially characterized in terms of a time-dependent periodic activation function to involve a chaotic dynamics on the basis of the energy steepest descent strategy. It is elucidated that the present neural network has an ability of the dynamic memory retrievals beyond the conventional models with the nonmonotonous activation function as well as such a monotonous activation function as sigmoidal one. This advantage is found to result from the nonmonotonous property of the analogue periodic mapping accompanied with a chaotic behaviour of the neurons. It is also found that the present analogue neuron model with the periodicity control has a remarkably large memory capacity in comparison with the previously proposed association models.

• 社団法人電子情報通信学会の論文
• 1997-10-20

著者

• NAKAGAWA Masahiro

  Division of Information Engineering, Department of Electrical Engineering, Faculty of Engineering, N

• Nakagawa M

  Nagaoka Univ. Technol. Niigata Jpn

関連論文

• Landau-de Gennes theory and nematodynamics (情報ディスプレイ)
• Periodic Chaos Neural Network with Autocorrelation Dynamics

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