Efficient optimisation of physical reservoir computers using only a delayed input
Enrico Picco, Lina Jaurigue, Kathy Lüdge, Serge Massar
Abstract
Reservoir computing is a machine learning algorithm for processing time dependent data which is well suited for experimental implementation. Tuning the hyperparameters of the reservoir is a time-consuming task that limits is applicability. Here we present an experimental validation of a recently proposed optimisation technique in which the reservoir receives both the input signal and a delayed version of the input signal. This augments the memory of the reservoir and improves its performance. It also simplifies the time-consuming task of hyperparameter tuning. The experimental system is an optoelectronic setup based on a fiber delay loop and a single nonlinear node. It is tested on several benchmark tasks and reservoir operating conditions. Our results demonstrate the effectiveness of the delayed input method for experimental implementation of reservoir computing systems. Picco et al experimentally validate an optimisation method for reservoir computers in which a delayed version of the input is provided. Their approach, validated on several benchmarks, improves performance and facilitates hyperparameter tuning.