A Parallel Graph Planarization Algorithm Using Gradient Ascent Learning of Hopfield Network
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概要
- 論文の詳細を見る
This paper proposes a gradient ascent learning algorithm of the Hopfield neural networks for graph planarization. This learning algorithm which is designed to embed a graph on a plane, uses the Hopfield neural network to get a near-maximal planar subgraph, and increase the energy by modifying weights 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 benchmark graphs up to 150 vertices and 1064 edges. The performance of the proposed 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 graphs.
- 社団法人 電気学会の論文
- 2003-03-01
著者
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TANG Zheng
Faculty of Engineering, Toyama University
-
Wang Rong
The Faculty Of Engineering Fukui University
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Wang Rong
Faculty Of Engineering Toyama University
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Cao Qi
Tateyama Systems Institute
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Tang Zheng
Faculty Of Engineering Miyazaki University
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Wang Rong
Faculty Of Engineering Fukui University
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Wang Ronglong
Faculty Of Engineering Fukui University
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