Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input
This work addresses an incremental improvement in optimization for reservoir computing, a domain-specific signal processing framework.
The paper tackled the challenge of optimizing reservoir computers by proposing a technique that uses only a delayed input signal to identify optimal operational regions, simplifying hyperparameter tuning, and validated it experimentally on benchmark tasks with unspecified performance gains.
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.