Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input

TL;DR

This paper experimentally validates a novel reservoir computing optimization method that uses only delayed input signals to efficiently find optimal operating conditions, simplifying hyperparameter tuning in signal processing tasks.

Contribution

It introduces a simple, delay-based optimization technique for reservoir computers and demonstrates its effectiveness across various tasks and conditions.

Findings

The delay-based method effectively identifies optimal reservoir parameters.

The approach simplifies hyperparameter tuning without sacrificing performance.

Validated on multiple benchmark tasks and reservoir configurations.

Abstract

We present an experimental validation of a recently proposed optimization technique for reservoir computing, using an optoelectronic setup. Reservoir computing is a robust framework for signal processing applications, and the development of efficient optimization approaches remains a key challenge. The technique we address leverages solely a delayed version of the input signal to identify the optimal operational region of the reservoir, simplifying the traditionally time-consuming task of hyperparameter tuning. We verify the effectiveness of this approach on different benchmark tasks and reservoir operating conditions.