窄带
油藏计算
计算机科学
自回归模型
参数化复杂度
公制(单位)
光子学
架空(工程)
任务(项目管理)
电子工程
实时计算
计算机工程
人工智能
算法
电信
光学
人工神经网络
物理
工程类
数学
运营管理
系统工程
计量经济学
操作系统
循环神经网络
作者
Benjamin H. Klimko,Haoying Dai,Yanne K. Chembo
出处
期刊:Optics Letters
[Optica Publishing Group]
日期:2024-05-31
卷期号:49 (13): 3608-3608
摘要
We experimentally investigate the performance of narrowband optoelectronic oscillator (OEO) reservoir computers using the standard 10th-order nonlinear autoregressive-moving-average (NARMA10) task. Because comparing results from differently parameterized photonic time-delay systems can be difficult, we introduce a new, to the best of our knowledge, metric that accounts for system size, computational accuracy, and training effort overhead in order to provide an “at-a-glance” method to holistically determine a reservoir computer’s performance. We then demonstrate the first experimental effort of narrowband OEO-based reservoir computing for the RADIOML dataset, which consists of recognizing and classifying IQ-modulated radio signals including analog and digital modulations. Our results indicate that narrowband OEOs are capable of achieving reasonable accuracies with exceptionally small training sets, thereby paving the way to real-time machine learning for radio frequency signals.
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