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Deep Echo State Networks in Industrial Applications
This paper analyzes the impact of reservoir computing, and, in particular, of Deep Echo State Networks, to the modeling of highly non-linear dynamical systems that can be commonly found in the industry. Several applications are presented focusing on forecasting models related to energy content of st...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256577/ http://dx.doi.org/10.1007/978-3-030-49186-4_5 |
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author | Dettori, Stefano Matino, Ismael Colla, Valentina Speets, Ramon |
author_facet | Dettori, Stefano Matino, Ismael Colla, Valentina Speets, Ramon |
author_sort | Dettori, Stefano |
collection | PubMed |
description | This paper analyzes the impact of reservoir computing, and, in particular, of Deep Echo State Networks, to the modeling of highly non-linear dynamical systems that can be commonly found in the industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions. |
format | Online Article Text |
id | pubmed-7256577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72565772020-05-29 Deep Echo State Networks in Industrial Applications Dettori, Stefano Matino, Ismael Colla, Valentina Speets, Ramon Artificial Intelligence Applications and Innovations Article This paper analyzes the impact of reservoir computing, and, in particular, of Deep Echo State Networks, to the modeling of highly non-linear dynamical systems that can be commonly found in the industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions. 2020-05-06 /pmc/articles/PMC7256577/ http://dx.doi.org/10.1007/978-3-030-49186-4_5 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Dettori, Stefano Matino, Ismael Colla, Valentina Speets, Ramon Deep Echo State Networks in Industrial Applications |
title | Deep Echo State Networks in Industrial Applications |
title_full | Deep Echo State Networks in Industrial Applications |
title_fullStr | Deep Echo State Networks in Industrial Applications |
title_full_unstemmed | Deep Echo State Networks in Industrial Applications |
title_short | Deep Echo State Networks in Industrial Applications |
title_sort | deep echo state networks in industrial applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256577/ http://dx.doi.org/10.1007/978-3-030-49186-4_5 |
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