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Reservoir Computing with Delayed Input for Fast and Easy Optimisation
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700644/ https://www.ncbi.nlm.nih.gov/pubmed/34945866 http://dx.doi.org/10.3390/e23121560 |
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author | Jaurigue, Lina Robertson, Elizabeth Wolters, Janik Lüdge, Kathy |
author_facet | Jaurigue, Lina Robertson, Elizabeth Wolters, Janik Lüdge, Kathy |
author_sort | Jaurigue, Lina |
collection | PubMed |
description | Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. 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. |
format | Online Article Text |
id | pubmed-8700644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87006442021-12-24 Reservoir Computing with Delayed Input for Fast and Easy Optimisation Jaurigue, Lina Robertson, Elizabeth Wolters, Janik Lüdge, Kathy Entropy (Basel) Article Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. 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. MDPI 2021-11-23 /pmc/articles/PMC8700644/ /pubmed/34945866 http://dx.doi.org/10.3390/e23121560 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jaurigue, Lina Robertson, Elizabeth Wolters, Janik Lüdge, Kathy Reservoir Computing with Delayed Input for Fast and Easy Optimisation |
title | Reservoir Computing with Delayed Input for Fast and Easy Optimisation |
title_full | Reservoir Computing with Delayed Input for Fast and Easy Optimisation |
title_fullStr | Reservoir Computing with Delayed Input for Fast and Easy Optimisation |
title_full_unstemmed | Reservoir Computing with Delayed Input for Fast and Easy Optimisation |
title_short | Reservoir Computing with Delayed Input for Fast and Easy Optimisation |
title_sort | reservoir computing with delayed input for fast and easy optimisation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700644/ https://www.ncbi.nlm.nih.gov/pubmed/34945866 http://dx.doi.org/10.3390/e23121560 |
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