Zhen-Ni Lu , Jing-Ting Ye , Zhong-Da Zhang , Jia-Wei Cai , Xiang-Yu Pan , Jian-Long Xu , Xu Gao , Ya-Nan Zhong , Sui-Dong Wang
{"title":"Memristor-based input delay reservoir computing system for temporal signal prediction","authors":"Zhen-Ni Lu , Jing-Ting Ye , Zhong-Da Zhang , Jia-Wei Cai , Xiang-Yu Pan , Jian-Long Xu , Xu Gao , Ya-Nan Zhong , Sui-Dong Wang","doi":"10.1016/j.mee.2024.112240","DOIUrl":null,"url":null,"abstract":"<div><p>Reservoir computing (RC) system, featured by its recursive structure, has been utilized for temporal signal processing, offering both low power consumption and high computational speed. This work reports on a novel input delay reservoir computing (ID-RC) system based on the oxide memristors, which can be applied to temporal signal prediction. The particle swarm optimization (PSO) algorithm is employed in the ID-RC system to obtain optimal hyperparameters for multi-step prediction in the Mackey-Glass task, with a normalized root-mean-square error (NRMSE) of only 0.09 at the 20th step. Significantly, by employing the ID-RC system in temporal signal prediction of the Hénon map and the nonlinear autoregressive moving average (NARMA10), small NRMSEs of 0.047 and 0.017 were achieved, respectively. The memristor-based ID-RC system turns out to be highly promising in forecasting of chaotic time series.</p></div>","PeriodicalId":18557,"journal":{"name":"Microelectronic Engineering","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microelectronic Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167931724001096","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Reservoir computing (RC) system, featured by its recursive structure, has been utilized for temporal signal processing, offering both low power consumption and high computational speed. This work reports on a novel input delay reservoir computing (ID-RC) system based on the oxide memristors, which can be applied to temporal signal prediction. The particle swarm optimization (PSO) algorithm is employed in the ID-RC system to obtain optimal hyperparameters for multi-step prediction in the Mackey-Glass task, with a normalized root-mean-square error (NRMSE) of only 0.09 at the 20th step. Significantly, by employing the ID-RC system in temporal signal prediction of the Hénon map and the nonlinear autoregressive moving average (NARMA10), small NRMSEs of 0.047 and 0.017 were achieved, respectively. The memristor-based ID-RC system turns out to be highly promising in forecasting of chaotic time series.
期刊介绍：
Microelectronic Engineering is the premier nanoprocessing, and nanotechnology journal focusing on fabrication of electronic, photonic, bioelectronic, electromechanic and fluidic devices and systems, and their applications in the broad areas of electronics, photonics, energy, life sciences, and environment. It covers also the expanding interdisciplinary field of "more than Moore" and "beyond Moore" integrated nanoelectronics / photonics and micro-/nano-/bio-systems. Through its unique mixture of peer-reviewed articles, reviews, accelerated publications, short and Technical notes, and the latest research news on key developments, Microelectronic Engineering provides comprehensive coverage of this exciting, interdisciplinary and dynamic new field for researchers in academia and professionals in industry.
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