A Neural Network Model for Generating Intermittent Chaos
Summary :
We derive the eigenvalue constraint for a neural network with three degrees of freedom. The result implies that the neural network needs a neuron with variable output function to generate chaos. It is also shown that the neuron with the special characteristics can be constructed by ordinary neurons.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.9 pp.1544-1547
- Publication Date
- 1993/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Neural Networks
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Hideo MATSUDA, Akihiko UCHIYAMA, "A Neural Network Model for Generating Intermittent Chaos" in IEICE TRANSACTIONS on Fundamentals,
vol. E76-A, no. 9, pp. 1544-1547, September 1993, doi: .
Abstract: We derive the eigenvalue constraint for a neural network with three degrees of freedom. The result implies that the neural network needs a neuron with variable output function to generate chaos. It is also shown that the neuron with the special characteristics can be constructed by ordinary neurons.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e76-a_9_1544/_p
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@ARTICLE{e76-a_9_1544,
author={Hideo MATSUDA, Akihiko UCHIYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Neural Network Model for Generating Intermittent Chaos},
year={1993},
volume={E76-A},
number={9},
pages={1544-1547},
abstract={We derive the eigenvalue constraint for a neural network with three degrees of freedom. The result implies that the neural network needs a neuron with variable output function to generate chaos. It is also shown that the neuron with the special characteristics can be constructed by ordinary neurons.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - A Neural Network Model for Generating Intermittent Chaos
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1544
EP - 1547
AU - Hideo MATSUDA
AU - Akihiko UCHIYAMA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1993
AB - We derive the eigenvalue constraint for a neural network with three degrees of freedom. The result implies that the neural network needs a neuron with variable output function to generate chaos. It is also shown that the neuron with the special characteristics can be constructed by ordinary neurons.
ER -
English
