WEKO3
アイテム
Self-Organizing Multiple Readouts for Reservoir Computing
http://hdl.handle.net/10228/0002001128
http://hdl.handle.net/10228/00020011286a1b6d3b-a525-49b5-9f23-b3d831259af5
| 名前 / ファイル | ライセンス | アクション |
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10444461.pdf (1.8 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||||
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| 公開日 | 2025-01-20 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Self-Organizing Multiple Readouts for Reservoir Computing | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
田中, 悠一朗
× 田中, 悠一朗
WEKO
30537
× 田向, 権
WEKO
6059
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| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2023 The Authors. | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||
| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | With advancements in deep learning (DL), artificial intelligence (AI) technology has become an indispensable tool. However, the application of DL incurs significant computational costs, making it less viable for edge AI scenarios. Consequently, the demand for cost-effective AI solutions, other than DL-based approaches, is increasing. Reservoir computing (RC) has attracted interest owing to its ability to provide low-cost training alternatives, holding great promise for edge AI applications. However, the training capability of RC is constrained by its reliance on a single linear layer, while weight connections in the remaining layers remain static during training. Moreover, accomplishing continuous learning tasks is difficult owing to the catastrophic forgetting in the linear layer. Therefore, we propose the integration of self-organizing multiple readouts to enhance RC’s training capability. Our method distributes training data across multiple readouts, which prevents catastrophic forgetting of readouts and empowers each readout to adeptly assimilate new data, thereby elevating the overall training performance. The self-organizing function, which assigns similar data to the same readout, optimizes the memory utilization of these multiple readouts. Experimental results show that an RC equipped with the proposed multiple readouts successfully solved a continuous learning task by mitigating catastrophic forgetting because of the data distribution to the multiple readouts. Additionally, the RC achieved higher accuracy in a sound recognition task compared with the existing RC paradigm because of ensemble learning in the multiple readouts. Multiple readouts are effective in enhancing the training capability of RC and can contribute to the realization of RC applications. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : IEEE Access 巻 11, p. 138839-138849, 発行日 2023-12-07 |
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| 出版者 | IEEE | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Catastrophic forgetting | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | continuous learning | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | echo state network | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | edge computing | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | ensemble learning | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | reservoir computing | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | self-organizing map | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | sound classification | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2023.3340311 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2169-3536 | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | no | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10444461 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 12629 | |||||||||||||
