油藏计算
实现(概率)
计算机科学
钻石
石墨烯
人工智能
计算科学
纳米技术
材料科学
人工神经网络
数学
统计
循环神经网络
复合材料
作者
Yuga Ito,Hiroshi Iwane,Siyu Jia,Ken‐ichi Ueda
标识
DOI:10.35848/1882-0786/ace8ef
摘要
Abstract Reservoir computing is one of the most promising machine learning architectures and could allow highly efficient, high-speed processing of time-series data. Physical reservoir computing based on various physical phenomena that exhibit complicated dynamics has been widely investigated in recent years. The present work demonstrates vertically aligned graphene/diamond junctions (photomemristors) could be employed for physical reservoir computing involving image recognition of single digits. Exceptional image recognition performance of 92% was obtained due to their complex photoconducting behaviors. This work is expected to assist in the realization of novel visual information processing systems using photomemristors that mimic human brain functions.
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