Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data
This work addresses the need for efficient and technically simple hardware implementations of reservoir computing systems, which is incremental as it builds on existing RC schemes.
The researchers tackled the problem of forecasting chaotic time series by experimentally validating a physical reservoir computing system that uses solitary waves and biologically-inspired nonlinear input transformations, demonstrating it as a simple hardware counterpart to next-generation RC algorithms with minimal computational power.
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.