Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Version 1 : Received: 31 October 2021 / Approved: 2 November 2021 / Online: 2 November 2021 (10:09:46 CET)

A peer-reviewed article of this Preprint also exists.

Jaurigue, L.; Robertson, E.; Wolters, J.; Lüdge, K. Reservoir Computing with Delayed Input for Fast and Easy Optimisation. Entropy 2021, 23, 1560. Jaurigue, L.; Robertson, E.; Wolters, J.; Lüdge, K. Reservoir Computing with Delayed Input for Fast and Easy Optimisation. Entropy 2021, 23, 1560.

Journal reference: Entropy 2021, 23, 1560
DOI: 10.3390/e23121560

Abstract

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

Keywords

reservoir computing; time series prediction; performance optimisation

Subject

PHYSICAL SCIENCES, General & Theoretical Physics

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