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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...

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Detalles Bibliográficos
Autores principales: Dettori, Stefano, Matino, Ismael, Colla, Valentina, Speets, Ramon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
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.
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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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