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Photonic extreme learning machine based on frequency multiplexing
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DOI：10.1364/OE.433535.png)
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Abstract
En 中文
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