A Hill-Shift Learning Algorithm of Hopfield Network for Bipartite Subgraph Problem(Neural Networks and Bioengineering)
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概要
- 論文の詳細を見る
In this paper, we present a hill-shift learning method of the Hopfield neural network for bipartite subgraph problem. The method uses the Hopfield neural network to get a near-maximum bipartite subgraph, and shifts the local minimum of energy function by adjusts the balance between two terms in the energy function to help the network escape from the state of the near-maximum bipartite subgraph to the state of the maximum bipartite subgraph or better one. A large number of instances are simulated to verify the proposed method with the simulation results showing that the solution quality is superior to that of best existing parallel algorithm.
- 社団法人電子情報通信学会の論文
- 2006-01-01
著者
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WANG Rong-Long
Faculty of Engineering, Fukui University
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Wang Rong-long
Faculty Of Engineering Toyama University
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Wang Rong-long
Faculty Of Engineering University Of Fukui
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OKAZAKI Kozo
Faculty of Engineering, University of Fukui
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Okazaki Kozo
Faculty Of Engineering University Of Fukui
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Okazaki Kozo
Univ. Of Fukui Fukui‐shi Jpn
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Wang Rong-long
Faculty Of Engineering Fukui University
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