Presentation 2014-01-21
逆関数ゼロ遅延モデルを用いたニューラルネットワークの学習
Yuta HORIUCHI, Yoshihiro HAYAKAWA, Shigeo SATO, Koji NAKAJIMA,
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Abstract(in English) The Inverse function Delayed (ID) model has been proposed as one of novel neural models. The ID model has an ability of oscillation, and this model can solve some local minimum problem in combinatorial optimization problems. However, ID model has large calculation cost, and it is difficult to apply for large size combinational optimization problems. This problem was solved by Inverse function Delay-Less (IDL) model in combinational optimization problems. But learning performance of IDL model has not been discussed yet. This study is to build a hierarchical network using by IDL model, and we derive back propagation learning with IDL model.
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Keyword(in English) Neural network / Hierarchical network / Inverse function / Back propagation learning
Paper # NLP2013-142
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Conference Information
Committee NLP
Conference Date 2014/1/14(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
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Title (in English)
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Keyword(1) Neural network
Keyword(2) Hierarchical network
Keyword(3) Inverse function
Keyword(4) Back propagation learning
1st Author's Name Yuta HORIUCHI
1st Author's Affiliation Laboratory for Brainware Research Institute of Electrical Communication, Tohoku University:Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication, Tohoku University()
2nd Author's Name Yoshihiro HAYAKAWA
2nd Author's Affiliation Sendai National College of Technology
3rd Author's Name Shigeo SATO
3rd Author's Affiliation Laboratory for Brainware Research Institute of Electrical Communication, Tohoku University:Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication, Tohoku University
4th Author's Name Koji NAKAJIMA
4th Author's Affiliation Laboratory for Brainware Research Institute of Electrical Communication, Tohoku University:Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication, Tohoku University
Date 2014-01-21
Paper # NLP2013-142
Volume (vol) vol.113
Number (no) 383
Page pp.pp.-
#Pages 4
Date of Issue