期刊
JAPANESE JOURNAL OF APPLIED PHYSICS
卷 59, 期 6, 页码 -出版社
IOP Publishing Ltd
DOI: 10.35848/1347-4065/ab8d4f
关键词
reservoir computing; physical system; nonlinear dynamics; machine learning; material
资金
- JSPS KAKENHI [JP18H05472]
- MEXT Quantum Leap Flagship Program (MEXT Q-LEAP) [JPMXS0118067394]
Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g. into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.
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