PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks

Producción Científica

Detalhes bibliográficos
Autores: Merayo Álvarez, Noemí, Juárez Estévez, David, Aguado Manzano, Juan Carlos, Miguel Jiménez, Ignacio de, Durán Barroso, Ramón José, Fernández Reguero, Patricia, Lorenzo Toledo, Rubén Mateo, Abril Domingo, Evaristo José
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/33487
Acesso em linha:https://doi.org/10.1364/JOCN.9.000433
http://uvadoc.uva.es/handle/10324/33487
Access Level:acceso abierto
Palavra-chave:Red neuronal (NN)
Red óptica pasiva (PON)
Neural network (NN)
Passive optical network (PON)
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spelling PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networksMerayo Álvarez, NoemíJuárez Estévez, DavidAguado Manzano, Juan CarlosMiguel Jiménez, Ignacio deDurán Barroso, Ramón JoséFernández Reguero, PatriciaLorenzo Toledo, Rubén MateoAbril Domingo, Evaristo JoséRed neuronal (NN)Red óptica pasiva (PON)Neural network (NN)Passive optical network (PON)Producción CientíficaIn this paper, a proportional-integral-derivative (PID) controller integrated with a neural network (NN) is proposed to ensure quality of service (QoS) bandwidth requirements in passive optical networks (PONs). To the best of our knowledge, this is the first time an approach that implements a NN to tune a PID to deal with QoS in PONs is used. In contrast to other tuning techniques such as Ziegler-Nichols or genetic algorithms (GA), our proposal allows a real-time adjustment of the tuning parameters according to the network conditions. Thus, the new algorithm provides an online control of the tuning process unlike the ZN and GA techniques, whose tuning parameters are calculated offline. The algorithm, called neural network service level PID (NNSPID), guarantees minimum bandwidth levels to users depending on their service level agreement, and it is compared with a tuning technique based on genetic algorithms (GASPID). The simulation study demonstrates that NN-SPID continuously adapts the tuning parameters, achieving lower fluctuations than GA-SPID in the allocation process. As a consequence, it provides a more stable response than GA-SPID since it needs to launch the GA to obtain new tuning values. Furthermore, NN-SPID guarantees the minimum bandwidth levels faster than GA-SPID. Finally, NN-SPID is more robust than GA-SPID under real-time changes of the guaranteed bandwidth levels, as GA-SPID shows high fluctuations in the allocated bandwidth, especially just after any change is made.Ministerio de Ciencia e Innovación (Projects TEC2014-53071-C3-2-P and TEC2015-71932-REDT)Institute of Electrical and Electronics Engineers (IEEE)2017info:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.1364/JOCN.9.000433http://uvadoc.uva.es/handle/10324/33487reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://ieeexplore.ieee.org/document/7926828info:eu-repo/semantics/openAccessoai:uvadoc.uva.es:10324/334872026-06-13T12:44:47Z
dc.title.none.fl_str_mv PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
title PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
spellingShingle PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
Merayo Álvarez, Noemí
Red neuronal (NN)
Red óptica pasiva (PON)
Neural network (NN)
Passive optical network (PON)
title_short PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
title_full PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
title_fullStr PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
title_full_unstemmed PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
title_sort PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks
dc.creator.none.fl_str_mv Merayo Álvarez, Noemí
Juárez Estévez, David
Aguado Manzano, Juan Carlos
Miguel Jiménez, Ignacio de
Durán Barroso, Ramón José
Fernández Reguero, Patricia
Lorenzo Toledo, Rubén Mateo
Abril Domingo, Evaristo José
author Merayo Álvarez, Noemí
author_facet Merayo Álvarez, Noemí
Juárez Estévez, David
Aguado Manzano, Juan Carlos
Miguel Jiménez, Ignacio de
Durán Barroso, Ramón José
Fernández Reguero, Patricia
Lorenzo Toledo, Rubén Mateo
Abril Domingo, Evaristo José
author_role author
author2 Juárez Estévez, David
Aguado Manzano, Juan Carlos
Miguel Jiménez, Ignacio de
Durán Barroso, Ramón José
Fernández Reguero, Patricia
Lorenzo Toledo, Rubén Mateo
Abril Domingo, Evaristo José
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Red neuronal (NN)
Red óptica pasiva (PON)
Neural network (NN)
Passive optical network (PON)
topic Red neuronal (NN)
Red óptica pasiva (PON)
Neural network (NN)
Passive optical network (PON)
description Producción Científica
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1364/JOCN.9.000433
http://uvadoc.uva.es/handle/10324/33487
url https://doi.org/10.1364/JOCN.9.000433
http://uvadoc.uva.es/handle/10324/33487
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://ieeexplore.ieee.org/document/7926828
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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