"Deterministic Diffusion" in a Neural Network Model
Summary :
This paper describes that a neural network, which consists of neurons with piecewise–linear sigmoid characteristics, is able to approximate any piecewise–linear map with origin symmetry. The neural network can generate "deterministic diffusion" originating from its diffusive trajectory.
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- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.11 pp.1879-1881
- Publication Date
- 1994/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section LETTER (Special Section of Letters Selected from the 1994 IEICE Spring Conference)
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Hideo MATSUDA, Akihiko UCHIYAMA, ""Deterministic Diffusion" in a Neural Network Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 11, pp. 1879-1881, November 1994, doi: .
Abstract: This paper describes that a neural network, which consists of neurons with piecewise–linear sigmoid characteristics, is able to approximate any piecewise–linear map with origin symmetry. The neural network can generate "deterministic diffusion" originating from its diffusive trajectory.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_11_1879/_p
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@ARTICLE{e77-a_11_1879,
author={Hideo MATSUDA, Akihiko UCHIYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={"Deterministic Diffusion" in a Neural Network Model},
year={1994},
volume={E77-A},
number={11},
pages={1879-1881},
abstract={This paper describes that a neural network, which consists of neurons with piecewise–linear sigmoid characteristics, is able to approximate any piecewise–linear map with origin symmetry. The neural network can generate "deterministic diffusion" originating from its diffusive trajectory.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - "Deterministic Diffusion" in a Neural Network Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1879
EP - 1881
AU - Hideo MATSUDA
AU - Akihiko UCHIYAMA
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 1994
AB - This paper describes that a neural network, which consists of neurons with piecewise–linear sigmoid characteristics, is able to approximate any piecewise–linear map with origin symmetry. The neural network can generate "deterministic diffusion" originating from its diffusive trajectory.
ER -
English
