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Paper Abstract and Keywords
Presentation 2024-06-22 11:15
Oscillation-driven reservoir computing for long-term timing/chaotic time-series prediction
Yuji Kawai (Osaka Univ.), Takashi Morita (Chubu Univ.), Park Jihoon (NICT/Osaka Univ.), Minoru Asada (IPUT/Osaka Univ./NICT/Chubu Univ.) NC2024-30 IBISML2024-30
Abstract (in Japanese) (See Japanese page) 
(in English) Reservoir computing has been exploited for model-free prediction of various time series, including chaotic dynamical systems; however, its instability limits the horizon of time series that can be accurately predicted. In this study, we propose a system in which oscillator (sinusoidal) inputs are fed into reservoir computing with output feedback to improve the stability of the system and enable it to long-term time-series prediction. Multiple oscillator inputs with various different frequencies allow the reservoir system to generate stable and complex dynamics, resulting in highly accurate prediction of target time series. The proposed system is evaluated in the timing learning task and the Lorenz time-series prediction task. The results showed that the proposed system could predict the timing for more than 120 s and accurately predicted the Lorenz time series for more than 20 s, whereas the time constant of the reservoir neuron model was 10 ms.
Keyword (in Japanese) (See Japanese page) 
(in English) reservoir computing / recurrent neural networks / oscillators / sine waves / timing learning / chaotic time series / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 85, NC2024-30, pp. 182-187, June 2024.
Paper # NC2024-30 
Date of Issue 2024-06-13 (NC, IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 NC2024-30 IBISML2024-30

Conference Information
Committee IBISML NC IPSJ-BIO IPSJ-MPS  
Conference Date 2024-06-20 - 2024-06-22 
Place (in Japanese) (See Japanese page) 
Place (in English) OIST 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2024-06-IBISML-NC-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Oscillation-driven reservoir computing for long-term timing/chaotic time-series prediction 
Sub Title (in English)  
Keyword(1) reservoir computing  
Keyword(2) recurrent neural networks  
Keyword(3) oscillators  
Keyword(4) sine waves  
Keyword(5) timing learning  
Keyword(6) chaotic time series  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuji Kawai  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Takashi Morita  
2nd Author's Affiliation Chubu University (Chubu Univ.)
3rd Author's Name Park Jihoon  
3rd Author's Affiliation National Institute of Information and Communications Technology/Osaka University (NICT/Osaka Univ.)
4th Author's Name Minoru Asada  
4th Author's Affiliation IPUT/Osaka University/NICT/Chubu University (IPUT/Osaka Univ./NICT/Chubu Univ.)
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Speaker Author-1 
Date Time 2024-06-22 11:15:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2024-30, IBISML2024-30 
Volume (vol) vol.124 
Number (no) no.85(NC), no.86(IBISML) 
Page pp.182-187 
#Pages 6 
Date of Issue 2024-06-13 (NC, IBISML) 

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