このアイテムのアクセス数: 260

このアイテムのファイル:
ファイル 記述 サイズフォーマット 
MEMS49605.2023.10052286.pdf1.62 MBAdobe 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

アイテムの詳細レコードを表示する

Export to RefWorks


出力フォーマット 


このリポジトリに保管されているアイテムはすべて著作権により保護されています。