Journal
CHAOS
Volume 30, Issue 1, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5120788
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Funding
- University of Maryland through the Minta Martin Fellowship
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The concept of reservoir computing emerged from a specific machine learning paradigm characterized by a three-layered architecture (input, reservoir, and output), where only the output layer is trained and optimized for a particular task. In recent years, this approach has been successfully implemented using various hardware platforms based on optoelectronic and photonic systems with time-delayed feedback. In this review, we provide a survey of the latest advances in this field, with some perspectives related to the relationship between reservoir computing, nonlinear dynamics, and network theory. Published under license by AIP Publishing.
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