Paper Abstract and Keywords |
Presentation |
2020-03-26 11:25
Reservoir computing using fluid motion Keita Kohashi, Masanobu Inubushi, Susumu Goto (Osaka Univ.) CCS2019-40 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Reservoir computing (RC) is a machine learning method using nonlinear dynamical systems, which is effective for time-series processing. As a novel method of Natural Computing, various physical phenomena as the nonlinear dynamics have been utilized in the framework of RC so far. However, little is known about information processing performance by RC with spatiotemporal dynamics. In this study, we show that RC using fluid motion as spatiotemporal dynamics is effective for a variety of machine learning tasks including time-series prediction and speech recognition. Moreover, we study a relationship between physical properties of fluid motion and information processing performance, and discuss key issues inherent in the implementation of RC with spatiotemporal dynamics. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Reservoir computing / Echo state network / Machine learning / Fluid motion / Lyapunov analysis / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 485, CCS2019-40, pp. 25-27, March 2020. |
Paper # |
CCS2019-40 |
Date of Issue |
2020-03-18 (CCS) |
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) |
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CCS2019-40 |
Conference Information |
Committee |
CCS |
Conference Date |
2020-03-25 - 2020-03-26 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hosei Univ. Ichigaya Campus |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Natural Computing, etc. |
Paper Information |
Registration To |
CCS |
Conference Code |
2020-03-CCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Reservoir computing using fluid motion |
Sub Title (in English) |
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Reservoir computing |
Keyword(2) |
Echo state network |
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Machine learning |
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Fluid motion |
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Lyapunov analysis |
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1st Author's Name |
Keita Kohashi |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Masanobu Inubushi |
2nd Author's Affiliation |
Osaka University (Osaka Univ.) |
3rd Author's Name |
Susumu Goto |
3rd Author's Affiliation |
Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-03-26 11:25:00 |
Presentation Time |
25 minutes |
Registration for |
CCS |
Paper # |
CCS2019-40 |
Volume (vol) |
vol.119 |
Number (no) |
no.485 |
Page |
pp.25-27 |
#Pages |
3 |
Date of Issue |
2020-03-18 (CCS) |
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