The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
우리는 조합 최적화 문제인 Box Puzzling Problem을 해결하는 히스테리시스 신경망을 제안합니다. 히스테리시스 신경망은 비선형 역학 문제의 솔루션을 검색합니다. 출력 벡터는 해와 일치하는 경우에만 안정적이 됩니다. 이 시스템은 문제의 제약 조건을 만족하지 않으면 결코 안정될 수 없습니다. 하드웨어 계산 시간을 추정한 결과 문제의 규모가 커질수록 수치적 계산 시간이 하드웨어 시간에 비해 극단적으로 증가한다는 것을 알 수 있다. 그러나 시스템에는 제한 주기가 발생할 가능성이 있습니다. Limit Cycle을 완전히 제거하는 것은 매우 어렵지만, 우리는 이 현상을 제거할 수 있는 몇 가지 방법을 제안합니다.
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Toshiya NAKAGUCHI, Shinya ISOME, Kenya JIN'NO, Mamoru TANAKA, "Box Puzzling Problem Solver by Hysteresis Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 9, pp. 2173-2181, September 2001, doi: .
Abstract: We propose hysteresis neural network solving combinatorial optimization problems, Box Puzzling Problem. Hysteresis neural network searches solutions of the problem with nonlinear dynamics. The output vector becomes stable only when it corresponds with a solution. This system does never become stable without satisfying constraints of the problem. After estimating hardware calculating time, we obtain that numerical calculating time increases extremely comparing with hardware time as problem's scale increases. However the system has possibility of limit cycle. Though it is very hard to remove limit cycle completely, we propose some methods to remove this phenomenon.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_9_2173/_p
부
@ARTICLE{e84-a_9_2173,
author={Toshiya NAKAGUCHI, Shinya ISOME, Kenya JIN'NO, Mamoru TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Box Puzzling Problem Solver by Hysteresis Neural Networks},
year={2001},
volume={E84-A},
number={9},
pages={2173-2181},
abstract={We propose hysteresis neural network solving combinatorial optimization problems, Box Puzzling Problem. Hysteresis neural network searches solutions of the problem with nonlinear dynamics. The output vector becomes stable only when it corresponds with a solution. This system does never become stable without satisfying constraints of the problem. After estimating hardware calculating time, we obtain that numerical calculating time increases extremely comparing with hardware time as problem's scale increases. However the system has possibility of limit cycle. Though it is very hard to remove limit cycle completely, we propose some methods to remove this phenomenon.},
keywords={},
doi={},
ISSN={},
month={September},}
부
TY - JOUR
TI - Box Puzzling Problem Solver by Hysteresis Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2173
EP - 2181
AU - Toshiya NAKAGUCHI
AU - Shinya ISOME
AU - Kenya JIN'NO
AU - Mamoru TANAKA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2001
AB - We propose hysteresis neural network solving combinatorial optimization problems, Box Puzzling Problem. Hysteresis neural network searches solutions of the problem with nonlinear dynamics. The output vector becomes stable only when it corresponds with a solution. This system does never become stable without satisfying constraints of the problem. After estimating hardware calculating time, we obtain that numerical calculating time increases extremely comparing with hardware time as problem's scale increases. However the system has possibility of limit cycle. Though it is very hard to remove limit cycle completely, we propose some methods to remove this phenomenon.
ER -