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
그래프 평탄화를 위한 Hopfield 신경망의 경사 상승 학습 알고리즘을 제시합니다. 이 학습 알고리즘은 Hopfield 신경망을 사용하여 최대값에 가까운 평면 하위 그래프를 얻고, 네트워크가 최대값에 가까운 평면 하위 그래프 상태에서 최대값에 가까운 상태로 탈출할 수 있도록 경사 상승 방향으로 매개변수를 수정하여 에너지를 증가시킵니다. 평면 하위 그래프 또는 더 나은 것. 제안된 알고리즘은 정점 150개, 간선 1064개까지의 여러 그래프에 적용된다. 우리 알고리즘의 성능은 Takefuji/Lee의 방법과 비교됩니다. 시뮬레이션 결과는 제안된 알고리즘이 테스트된 모든 그래프의 솔루션 품질 측면에서 Takefuji/Lee의 방법보다 훨씬 우수하다는 것을 보여줍니다.
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Zheng TANG, Rong Long WANG, Qi Ping CAO, "A Hopfield Network Learning Algorithm for Graph Planarization" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 7, pp. 1799-1802, July 2001, doi: .
Abstract: A gradient ascent learning algorithm of the Hopfield neural networks for graph planarization is presented. This learning algorithm uses the Hopfield neural network to get a near-maximal planar subgraph, and increases the energy by modifying parameters in a gradient ascent direction to help the network escape from the state of the near-maximal planar subgraph to the state of the maximal planar subgraph or better one. The proposed algorithm is applied to several graphs up to 150 vertices and 1064 edges. The performance of our algorithm is compared with that of Takefuji/Lee's method. Simulation results show that the proposed algorithm is much better than Takefuji/Lee's method in terms of the solution quality for every tested graph.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_7_1799/_p
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@ARTICLE{e84-a_7_1799,
author={Zheng TANG, Rong Long WANG, Qi Ping CAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Hopfield Network Learning Algorithm for Graph Planarization},
year={2001},
volume={E84-A},
number={7},
pages={1799-1802},
abstract={A gradient ascent learning algorithm of the Hopfield neural networks for graph planarization is presented. This learning algorithm uses the Hopfield neural network to get a near-maximal planar subgraph, and increases the energy by modifying parameters in a gradient ascent direction to help the network escape from the state of the near-maximal planar subgraph to the state of the maximal planar subgraph or better one. The proposed algorithm is applied to several graphs up to 150 vertices and 1064 edges. The performance of our algorithm is compared with that of Takefuji/Lee's method. Simulation results show that the proposed algorithm is much better than Takefuji/Lee's method in terms of the solution quality for every tested graph.},
keywords={},
doi={},
ISSN={},
month={July},}
부
TY - JOUR
TI - A Hopfield Network Learning Algorithm for Graph Planarization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1799
EP - 1802
AU - Zheng TANG
AU - Rong Long WANG
AU - Qi Ping CAO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2001
AB - A gradient ascent learning algorithm of the Hopfield neural networks for graph planarization is presented. This learning algorithm uses the Hopfield neural network to get a near-maximal planar subgraph, and increases the energy by modifying parameters in a gradient ascent direction to help the network escape from the state of the near-maximal planar subgraph to the state of the maximal planar subgraph or better one. The proposed algorithm is applied to several graphs up to 150 vertices and 1064 edges. The performance of our algorithm is compared with that of Takefuji/Lee's method. Simulation results show that the proposed algorithm is much better than Takefuji/Lee's method in terms of the solution quality for every tested graph.
ER -