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ファイル | 記述 | サイズ | フォーマット | |
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MEMS49605.2023.10052286.pdf | 1.62 MB | Adobe PDF | 見る/開く |
タイトル: | Physical Reservoir Computing Using Nonlinear MEMS Resonator Having High Memory Capacity at "Edge of Chaos" |
著者: | Takemura, Hiroki Mizumoto, Takahiro Banerjee, Amit Hirotani, Jun Tsuchiya, Toshiyuki |
著者名の別形: | 竹村, 拓樹 水本, 昂宏 廣谷, 潤 土屋, 智由 |
キーワード: | Physical reservoir computing electrostatic nonlinear resonator machine learning silicon-on-insulator |
発行日: | 2023 |
出版者: | IEEE |
誌名: | 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS) |
開始ページ: | 515 |
終了ページ: | 518 |
抄録: | This paper reports physical reservoir computing (PRC) using a single nonlinear electrostatic resonator and demonstrates its high memory capacity at "edge of chaos." The resonator is a simple doubly supported resonator fabricated from a silicon-on-insulator wafer. We proposed a PRC system without feedback loop, in which its memory capacity relies on the decay time of the high-Q resonator. The benchmark task results indicate that the system shows good linear and nonlinear memory capacities at the resonance and the maximum capacity was obtained at the vicinity of the instability edge of the frequency response. |
記述: | 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS), 15-19 Jan. 2023. |
著作権等: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/284807 |
DOI(出版社版): | 10.1109/MEMS49605.2023.10052286 |
出現コレクション: | 学術雑誌掲載論文等 |

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