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Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data
Version 1
: Received: 4 January 2024 / Approved: 4 January 2024 / Online: 4 January 2024 (14:14:50 CET)
A peer-reviewed article of this Preprint also exists.
Maksymov, I.S. Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data. Dynamics 2024, 4, 119-134. Maksymov, I.S. Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data. Dynamics 2024, 4, 119-134.
Abstract
Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation’ improvement of the traditional RC algorithm.
Keywords
artificial intelligence; chaotic time series; fluid dynamics; nonlinear dynamics; reservoir computing; solitary waves.
Subject
Physical Sciences, Applied Physics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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