期刊
OPTICS EXPRESS
卷 29, 期 18, 页码 28257-28276出版社
OPTICAL SOC AMER
DOI: 10.1364/OE.433535
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资金
- H2020 Marie Sklodowska-Curie Actions (Project POSTDIGITAL) [860830]
- Fonds De La Recherche Scientifique - FNRS
The optical domain is a promising field for implementing neural networks due to its speed and parallelism. Extreme Learning Machines (ELMs) are feed-forward neural networks where only output weights are trained, while internal connections remain untrained. Experimental results show that photonic ELM performs well in classification tasks and nonlinear channel equalization tasks.
The optical domain is a promising field for the physical implementation of neural networks, due to the speed and parallelism of optics. Extreme learning machines (ELMs) are feed-forward neural networks in which only output weights are trained, while internal connections are randomly selected and left untrained. Here we report on a photonic ELM based on a frequency-multiplexed fiber setup. Multiplication by output weights can be performed either offline on a computer or optically by a programmable spectral filter. We present both numerical simulations and experimental results on classification tasks and a nonlinear channel equalization task. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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