IEICE TRANSACTIONS on Fundamentals
An Analysis on Minimum Searching Principle of Chaotic Neural Network
Summary :
This article analyzes dynamics of the chaotic neural network and minimum searching principle of this network. First it is indicated that the dynamics of the chaotic newral network is described like a gradient decent, and the chaotic neural network can roughly find out a local minimum point of a quadratic function using its attractor. Secondly It is guaranteed that the vertex corresponding a local minimum point derived from the chaotic neural network has a lower value of the objective function. Then it is confirmed that the chaotic neural network can escape an invalid local minimum and find out a reasonable one.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.3 pp.363-369
- Publication Date
- 1996/03/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section of Selected Papers from the 8th Karuizawa Workshop on Circuits and Systems)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Masaya OHTA, Kazumichi MATSUMIYA, Akio OGIHARA, Shinobu TAKAMATSU, Kunio FUKUNAGA, "An Analysis on Minimum Searching Principle of Chaotic Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 3, pp. 363-369, March 1996, doi: .
Abstract: This article analyzes dynamics of the chaotic neural network and minimum searching principle of this network. First it is indicated that the dynamics of the chaotic newral network is described like a gradient decent, and the chaotic neural network can roughly find out a local minimum point of a quadratic function using its attractor. Secondly It is guaranteed that the vertex corresponding a local minimum point derived from the chaotic neural network has a lower value of the objective function. Then it is confirmed that the chaotic neural network can escape an invalid local minimum and find out a reasonable one.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_3_363/_p
Copy
@ARTICLE{e79-a_3_363,
author={Masaya OHTA, Kazumichi MATSUMIYA, Akio OGIHARA, Shinobu TAKAMATSU, Kunio FUKUNAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Analysis on Minimum Searching Principle of Chaotic Neural Network},
year={1996},
volume={E79-A},
number={3},
pages={363-369},
abstract={This article analyzes dynamics of the chaotic neural network and minimum searching principle of this network. First it is indicated that the dynamics of the chaotic newral network is described like a gradient decent, and the chaotic neural network can roughly find out a local minimum point of a quadratic function using its attractor. Secondly It is guaranteed that the vertex corresponding a local minimum point derived from the chaotic neural network has a lower value of the objective function. Then it is confirmed that the chaotic neural network can escape an invalid local minimum and find out a reasonable one.},
keywords={},
doi={},
ISSN={},
month={March},}
Copy
TY - JOUR
TI - An Analysis on Minimum Searching Principle of Chaotic Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 363
EP - 369
AU - Masaya OHTA
AU - Kazumichi MATSUMIYA
AU - Akio OGIHARA
AU - Shinobu TAKAMATSU
AU - Kunio FUKUNAGA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 1996
AB - This article analyzes dynamics of the chaotic neural network and minimum searching principle of this network. First it is indicated that the dynamics of the chaotic newral network is described like a gradient decent, and the chaotic neural network can roughly find out a local minimum point of a quadratic function using its attractor. Secondly It is guaranteed that the vertex corresponding a local minimum point derived from the chaotic neural network has a lower value of the objective function. Then it is confirmed that the chaotic neural network can escape an invalid local minimum and find out a reasonable one.
ER -
English
