Efficient optimisation of physical reservoir computers using only a delayed input.

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

SUBMITTER: Picco E 

PROVIDER: S-EPMC11742992 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature