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Paper Abstract and Keywords
Presentation 2022-01-22 10:30
On the relationship between properties of the hysteresis reservoir layer and the training output sequence
Tsukasa Saito, Kenya Jin'no (Tokyo City Univ) NLP2021-99 MICT2021-74 MBE2021-60
Abstract (in Japanese) (See Japanese page) 
(in English) Reservoir computing is a type of machine learning model that can be trained at low cost and fast. However, conventional reservoir computing often do not achieve the memory capacity and nonlinearity required. To solve this prob- lem, we proposed hysteresis reservoir computing, a model in which conventional reservoir neurons are replaced by hysteresis neurons, which generate various output sequences by changing parameters. In this paper, we confirm the dynamics generated by changing the parameters of the hysteresis element in the hysteresis reservoir layer. The experimental results indicate that changing the parameters improves the learning ability and can represent specific series of data.
Keyword (in Japanese) (See Japanese page) 
(in English) reservoir computing / time series / oscillator / time constant / complex / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 335, NLP2021-99, pp. 121-124, Jan. 2022.
Paper # NLP2021-99 
Date of Issue 2022-01-14 (NLP, MICT, MBE) 
ISSN 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 NLP2021-99 MICT2021-74 MBE2021-60

Conference Information
Committee NLP MICT MBE NC  
Conference Date 2022-01-21 - 2022-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2022-01-NLP-MICT-MBE-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On the relationship between properties of the hysteresis reservoir layer and the training output sequence 
Sub Title (in English)  
Keyword(1) reservoir computing  
Keyword(2) time series  
Keyword(3) oscillator  
Keyword(4) time constant  
Keyword(5) complex  
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Keyword(8)  
1st Author's Name Tsukasa Saito  
1st Author's Affiliation Tokyo City University (Tokyo City Univ)
2nd Author's Name Kenya Jin'no  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ)
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Speaker Author-1 
Date Time 2022-01-22 10:30:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2021-99, MICT2021-74, MBE2021-60 
Volume (vol) vol.121 
Number (no) no.335(NLP), no.336(MICT), no.337(MBE) 
Page pp.121-124 
#Pages 4 
Date of Issue 2022-01-14 (NLP, MICT, MBE) 

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