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
본 논문에서는 뉴런 필터가 조합 최적화 문제에 대한 Hopfield 신경망의 계수 민감도를 완화하는 데 효과적이라는 것을 보여줍니다. 운동방정식의 매개변수는 신경망의 성능에 큰 영향을 미치기 때문에 매개변수의 값을 결정하는 것을 지원하기 위한 많은 연구가 진행되어 왔다. 그러나 아직까지 매개변수의 값을 실험적으로 결정한 연구자는 적지 않다. 우리는 뉴런 필터의 사용이 매개변수 조정, 특히 시뮬레이션을 통해 실험적으로 값을 결정하는 데 효과적이라는 것을 보여줍니다.
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Yoichi TAKENAKA, Nobuo FUNABIKI, Teruo HIGASHINO, "Relaxation of Coefficient Sensitiveness to Performance for Neural Networks Using Neuron Filter through Total Coloring Problems" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 9, pp. 2367-2370, September 2001, doi: .
Abstract: In this paper we show that the neuron filter is effective for relaxing the coefficient sensitiveness of the Hopfield neural network for combinatorial optimization problems. Since the parameters in motion equation have a significant influence on the performance of the neural network, many studies have been carried out to support determining the value of the parameters. However, not a few researchers have determined the value of the parameters experimentally yet. We show that the use of the neuron filter is effective for the parameter tuning, particularly for determining their values experimentally through simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_9_2367/_p
부
@ARTICLE{e84-a_9_2367,
author={Yoichi TAKENAKA, Nobuo FUNABIKI, Teruo HIGASHINO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Relaxation of Coefficient Sensitiveness to Performance for Neural Networks Using Neuron Filter through Total Coloring Problems},
year={2001},
volume={E84-A},
number={9},
pages={2367-2370},
abstract={In this paper we show that the neuron filter is effective for relaxing the coefficient sensitiveness of the Hopfield neural network for combinatorial optimization problems. Since the parameters in motion equation have a significant influence on the performance of the neural network, many studies have been carried out to support determining the value of the parameters. However, not a few researchers have determined the value of the parameters experimentally yet. We show that the use of the neuron filter is effective for the parameter tuning, particularly for determining their values experimentally through simulations.},
keywords={},
doi={},
ISSN={},
month={September},}
부
TY - JOUR
TI - Relaxation of Coefficient Sensitiveness to Performance for Neural Networks Using Neuron Filter through Total Coloring Problems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2367
EP - 2370
AU - Yoichi TAKENAKA
AU - Nobuo FUNABIKI
AU - Teruo HIGASHINO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2001
AB - In this paper we show that the neuron filter is effective for relaxing the coefficient sensitiveness of the Hopfield neural network for combinatorial optimization problems. Since the parameters in motion equation have a significant influence on the performance of the neural network, many studies have been carried out to support determining the value of the parameters. However, not a few researchers have determined the value of the parameters experimentally yet. We show that the use of the neuron filter is effective for the parameter tuning, particularly for determining their values experimentally through simulations.
ER -