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Presentation 2018-11-05 15:10
[Poster Presentation] Nonlinear Time Series Prediction using Multi-Step Learning Echo State Networks
Takanori Akiyama, Gouhei Tanaka (Tokyo Univ.) IBISML2018-83
Abstract (in Japanese) (See Japanese page) 
(in English) Reservoir Computing (RC) has recently attracted much attention as brain-like information processing for high-speed learning. RC has been applied to a variety of tasks. Especially, Jaeger proposed the Echo State Network (ESN) , which is one of the RC models, was efficient for chaotic time series prediction. However, there are two critical problems in nonlinear time series prediction using the ESN. One is that its prediction ability reaches the peak despite the increase in reservoir size. The other is that its prediction ability depends heavily on hyper parameters. In this research, we propose a multi-step learning ESN that can solve these problems. The proposed system has multiple reservoirs and the prediction error of one predictor is corrected by the other predictor. In this research, we demonstrated the efficiency of the proposed method in nonlinear time series prediction tasks and confirmed that the ability of the proposed method is robust against the change of hyper parameters using Lyapunov exponents.
Keyword (in Japanese) (See Japanese page) 
(in English) Reservoir computing / Echo state network / Multi-step learning / Nonlinear time series prediction / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 284, IBISML2018-83, pp. 293-299, Nov. 2018.
Paper # IBISML2018-83 
Date of Issue 2018-10-29 (IBISML) 
ISSN Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee IBISML  
Conference Date 2018-11-05 - 2018-11-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Citizens Activites Center (Kaderu 2.7) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2018) 
Paper Information
Registration To IBISML 
Conference Code 2018-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Nonlinear Time Series Prediction using Multi-Step Learning Echo State Networks 
Sub Title (in English)  
Keyword(1) Reservoir computing  
Keyword(2) Echo state network  
Keyword(3) Multi-step learning  
Keyword(4) Nonlinear time series prediction  
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1st Author's Name Takanori Akiyama  
1st Author's Affiliation Tokyo University (Tokyo Univ.)
2nd Author's Name Gouhei Tanaka  
2nd Author's Affiliation Tokyo University (Tokyo Univ.)
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Speaker Author-1 
Date Time 2018-11-05 15:10:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2018-83 
Volume (vol) vol.118 
Number (no) no.284 
Page pp.293-299 
#Pages
Date of Issue 2018-10-29 (IBISML) 


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