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.
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Research Article| January 08 2020
Machine learning based on reservoir computing with time-delayed optoelectronic and photonic systems
Yanne K. Chembo
Yanne K. Chembo; Machine learning based on reservoir computing with time-delayed optoelectronic and photonic systems. Chaos 1 January 2020; 30 (1): 013111. https://doi.org/10.1063/1.5120788
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