IEICE TRANSACTIONS on Fundamentals
An Autocorrelation Associative Neural Network with Self-Feedbacks
Summary :
In this article, the autocorrelation associative neural network that is one of well-known applications of neural networks is improved to extend its capacity and error correcting ability. Our approach of the improvement is based on the consideration that negative self-feedbacks remove spurious states. Therefore, we propose a method to determine the self-feedbacks as small as possible within the range that all stored patterns are stable. A state transition rule that enables to escape oscillation is also presented because the method has a possibility of falling into oscillation. The efficiency of the method is confirmed by means of some computer simulations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.12 pp.2072-2075
- Publication Date
- 1993/12/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section LETTER (Special Section of Letters Selected from the 1993 IEICE Fall Conference)
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Hiroshi UEDA, Masaya OHTA, Akio OGIHARA, Kunio FUKUNAGA, "An Autocorrelation Associative Neural Network with Self-Feedbacks" in IEICE TRANSACTIONS on Fundamentals,
vol. E76-A, no. 12, pp. 2072-2075, December 1993, doi: .
Abstract: In this article, the autocorrelation associative neural network that is one of well-known applications of neural networks is improved to extend its capacity and error correcting ability. Our approach of the improvement is based on the consideration that negative self-feedbacks remove spurious states. Therefore, we propose a method to determine the self-feedbacks as small as possible within the range that all stored patterns are stable. A state transition rule that enables to escape oscillation is also presented because the method has a possibility of falling into oscillation. The efficiency of the method is confirmed by means of some computer simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e76-a_12_2072/_p
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@ARTICLE{e76-a_12_2072,
author={Hiroshi UEDA, Masaya OHTA, Akio OGIHARA, Kunio FUKUNAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Autocorrelation Associative Neural Network with Self-Feedbacks},
year={1993},
volume={E76-A},
number={12},
pages={2072-2075},
abstract={In this article, the autocorrelation associative neural network that is one of well-known applications of neural networks is improved to extend its capacity and error correcting ability. Our approach of the improvement is based on the consideration that negative self-feedbacks remove spurious states. Therefore, we propose a method to determine the self-feedbacks as small as possible within the range that all stored patterns are stable. A state transition rule that enables to escape oscillation is also presented because the method has a possibility of falling into oscillation. The efficiency of the method is confirmed by means of some computer simulations.},
keywords={},
doi={},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - An Autocorrelation Associative Neural Network with Self-Feedbacks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2072
EP - 2075
AU - Hiroshi UEDA
AU - Masaya OHTA
AU - Akio OGIHARA
AU - Kunio FUKUNAGA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 1993
AB - In this article, the autocorrelation associative neural network that is one of well-known applications of neural networks is improved to extend its capacity and error correcting ability. Our approach of the improvement is based on the consideration that negative self-feedbacks remove spurious states. Therefore, we propose a method to determine the self-feedbacks as small as possible within the range that all stored patterns are stable. A state transition rule that enables to escape oscillation is also presented because the method has a possibility of falling into oscillation. The efficiency of the method is confirmed by means of some computer simulations.
ER -
English
