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| Presentation | 2022-03-27 10:50 An Improvement of Prediction Performance of Reservoir Computing using Deep FORCE Learning Kazuki Nakada, Eiji Suzuki, Keita Suda, Yukio Terasaki (TDK), Tetsuya Asai (Hokkaido Univ.), Tomoyuki Sasaki (TDK)  CCS2021-40 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The physical implementation has become increasingly important in the recent machine learning trends. Reservoir Computing (RC) is a machine learning framework for time series processing, and the research on RC has progressed rapidly because of the suitability for physical implementation. For implementing RC as edge AI devices, sequential online learning is an essential requirement. Previously, FORCE (First-Order, Reduced and Controlled Error) learning having fast convergence property has been proposed and its advantages over the conventional Stochastic Gradient Descent algorithm were demonstrated. However, RC is not suitable for incremental learning of multi-class data due to the restriction of a single output per layer since it is based on RLS (Recursive Least Squares) algorithm. In this report, we propose a natural extension of FORCE learning, Deep FORCE, based on Kalman filtering. We demonstrated that Deep FORCE can improve the prediction performance of real trajectory of a double pendulum as a nonlinear transformation task as compared to conventional FORCE learning. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Reservoir Computing / Online leaning / FORCE learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 442, CCS2021-40, pp. 25-30, March 2022. | |
| Paper # | CCS2021-40 | |
| Date of Issue | 2022-03-20 (CCS) | |
| 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 | CCS2021-40 |
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| Conference Information | |
| Committee | CCS |
| Conference Date | 2022-03-27 - 2022-03-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | RUSUTSU RESORT HOTEL & CONVENTION |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | CCS |
| Conference Code | 2022-03-CCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Improvement of Prediction Performance of Reservoir Computing using Deep FORCE Learning |
| Sub Title (in English) | |
| Keyword(1) | Reservoir Computing |
| Keyword(2) | Online leaning |
| Keyword(3) | FORCE learning |
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| 1st Author's Name | Kazuki Nakada |
| 1st Author's Affiliation | TDK Corporation (TDK) |
| 2nd Author's Name | Eiji Suzuki |
| 2nd Author's Affiliation | TDK Corporation (TDK) |
| 3rd Author's Name | Keita Suda |
| 3rd Author's Affiliation | TDK Corporation (TDK) |
| 4th Author's Name | Yukio Terasaki |
| 4th Author's Affiliation | TDK Corporation (TDK) |
| 5th Author's Name | Tetsuya Asai |
| 5th Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 6th Author's Name | Tomoyuki Sasaki |
| 6th Author's Affiliation | TDK Corporation (TDK) |
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| Speaker | Author-1 |
| Date Time | 2022-03-27 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | CCS |
| Paper # | CCS2021-40 |
| Volume (vol) | vol.121 |
| Number (no) | no.442 |
| Page | pp.25-30 |
| #Pages | 6 |
| Date of Issue | 2022-03-20 (CCS) |
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