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Paper Abstract and Keywords
Presentation 2014-06-30 16:00
Backpropagation learning using inverse function delay-less model
Yuta Horiuchi (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-25
Abstract (in Japanese) (See Japanese page) 
(in English) The Inverse function Delayed (ID) model has been proposed as one of novel neural models. ID model has a oscillation capacity. This model can solve some local minimum problem in combinatorial optimization problems. However, The drawback of the ID model is large calculation cost. So it is difficult to apply for large size combinational optimization problems. This problem was solved by Inverse function Delay-Less (IDL) model in com- binational optimization problems. We had derived back propagation learning with IDL model at continuous-time. However, the most popular BP learn in discrete-time. So this study is to build discrete-time IDL model BP learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural network / Hierarchical network / Inverse function / Back propagation learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 113, NLP2014-25, pp. 27-30, June 2014.
Paper # NLP2014-25 
Date of Issue 2014-06-23 (NLP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLP2014-25

Conference Information
Committee NLP  
Conference Date 2014-06-30 - 2014-07-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2014-06-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Backpropagation learning using inverse function delay-less model 
Sub Title (in English)  
Keyword(1) Neural network  
Keyword(2) Hierarchical network  
Keyword(3) Inverse function  
Keyword(4) Back propagation learning  
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1st Author's Name Yuta Horiuchi  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Yoshihiro Hayakawa  
2nd Author's Affiliation Sendai national college of technology (SNCT)
3rd Author's Name Takeshi Onomi  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
4th Author's Name Koji Nakajima  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2014-06-30 16:00:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2014-25 
Volume (vol) vol.114 
Number (no) no.113 
Page pp.27-30 
#Pages 4 
Date of Issue 2014-06-23 (NLP) 

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