  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.
