Connecting reservoir computing with statistical forecasting and deep neural networks

被引：0
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作者：
Lina Jaurigue
Kathy Lüdge
机构：
[1] Institut für Theoretische Physik,Technische Universität Berlin
[2] Institut für Physik,Technische Universität Ilmenau
来源：
Nature Communications | / 13卷
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摘要：
Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.
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