Connecting reservoir computing with statistical forecasting and deep neural networks

被引：17
|
作者：
Jaurigue, Lina [1 ]
Luedge, Kathy [2 ]
机构：
[1] Tech Univ Berlin, Inst Theoret Phys, Hardenbergstr 36, D-10623 Berlin, Germany
[2] Tech Univ Ilmenau, Inst Phys, Weimarer Str 25, D-98693 Ilmenau, Germany
来源：
NATURE COMMUNICATIONS | 2022年 / 13卷 / 01期
关键词：
D O I：
10.1038/s41467-021-27715-5
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
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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页数：3
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