Presentation 1997/10/20
Periodic Chaos Neural Network with Autocorrelation Dynamics
MASAHIRO NAKAGAWA,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) 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.
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Keyword(in English) chaos neuron / periodic mapping / associative memory
Paper # NC97-40
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Committee NC
Conference Date 1997/10/20(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Periodic Chaos Neural Network with Autocorrelation Dynamics
Sub Title (in English)
Keyword(1) chaos neuron
Keyword(2) periodic mapping
Keyword(3) associative memory
1st Author's Name MASAHIRO NAKAGAWA
1st Author's Affiliation Division of Information System Engineering, Faculty of Engineering, Nagaoka University of Technology()
Date 1997/10/20
Paper # NC97-40
Volume (vol) vol.97
Number (no) 332
Page pp.pp.-
#Pages 8
Date of Issue