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Paper Abstract and Keywords
Presentation 2008-08-04 15:30
A Hybrid PSO and quasi-Newton Technique for Training of Feedforward Neural Networks
Hiroshi Ninomiya (Shonan Inst. of Tech.), Qi-Jun Zhang (Carleton Univ.) CST2008-17
Abstract (in Japanese) (See Japanese page) 
(in English) This paper describes a new technique for training feedforward neural networks. We employ the proposed algorithm for robust neural network training purpose. Conventional neural network training algorithms based on the gradient descent often encounter local minima problems. Recently, some evolutionary algorithms are getting a lot more attention about global search ability but are less-accurate for complicated training task of neural networks. The proposed technique hybridizes local training algorithm based on quasi-Newton method with a recent global optimization algorithm called Particle Swarm Optimization (PSO). The proposed technique provides higher global convergence property than the conventional global optimization technique. Neural network training for some benchmark problems is presented to demonstrate the proposed algorithm. The proposed algorithm achieves more accurate and robust training results than the quasi-Newton method and the conventional PSOs.
Keyword (in Japanese) (See Japanese page) 
(in English) Feedforward neural networks / Particle swarm optimization / quasi-Newton method / Hybrid algorithm / / / /  
Reference Info. IEICE Tech. Rep., vol. 108, Aug. 2008.
Paper #  
Date of Issue 2008-07-28 (CST) 
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 CST2008-17

Conference Information
Committee MSS  
Conference Date 2008-08-04 - 2008-08-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Shizuoka University (Hamamatsu Campus), Faculty of Engineering 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Concurrent Systems 
Paper Information
Registration To MSS 
Conference Code 2008-08-CST 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Hybrid PSO and quasi-Newton Technique for Training of Feedforward Neural Networks 
Sub Title (in English)  
Keyword(1) Feedforward neural networks  
Keyword(2) Particle swarm optimization  
Keyword(3) quasi-Newton method  
Keyword(4) Hybrid algorithm  
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1st Author's Name Hiroshi Ninomiya  
1st Author's Affiliation Shonan Institute of Technology (Shonan Inst. of Tech.)
2nd Author's Name Qi-Jun Zhang  
2nd Author's Affiliation Carleton University (Carleton Univ.)
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Speaker Author-1 
Date Time 2008-08-04 15:30:00 
Presentation Time 25 minutes 
Registration for MSS 
Paper # CST2008-17 
Volume (vol) vol.108 
Number (no) no.176 
Page pp.29-34 
#Pages 6 
Date of Issue 2008-07-28 (CST) 

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