Maximum-Flow Neural Network : A Novel Neural Network for the Maximum Flow Problem
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概要
- 論文の詳細を見る
In advance of network communication society by the internet, the way how to send data fast with a little loss becomes an important transportation problem. A generalized maximum flow algorithm gives the best solution for the transportation problem that which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm is growing more and more. In this paper, we propose a Maximum-Flow Neural Network (MF-NN) in which branch nonlinearity has a saturation characteristic and by which the maximum flow problem can be solved with analog high-speed parallel processing. That is, the proposed neural network for the maximum flow problem can be realized by a nonlinear resistive circuit where each connection weight between nodal neurons has a sigmodal or piece-wise linear function. The parallel hardware of the MF-NN will be easily implemented.
- （社）電子情報通信学会の論文
- 2009-04-01
著者
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SATO Masatoshi
Department of Physics,Division of Material Science,Nagoya University
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TANAKA Mamoru
Department of Electrical and Electronics Engineering, Sophia University
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Tanaka Mamoru
Sophia University
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Aomori Hisashi
Department Of Electrical And Electronics Engineering Sophia University
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Aomori Hisashi
Tokyo Univ. Sci. Chiba Jpn
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Sato Masatoshi
Department Of Electrical And Electronics Engineering Sophia University
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Tanaka Mamoru
Department Of Electrical And Electronics Engineering Sophia University
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Tanaka Mamoru
Department Of Applied Chemistry Kobe City College Of Technology
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Sato Masatoshi
Department Of Agro-environmental Science Obihiro University Of Agriculture And Veterinary Medicine
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