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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.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.7 pp.1799-1802

- Publication Date
- 2001/07/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- LETTER

- Category
- Neural Networks and Bioengineering

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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},}

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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 -