Echo State Networks For Seasonal Streamflow Series Forecasting

dc.creatorSiqueira H.
dc.creatorBoccato L.
dc.creatorAttux R.
dc.creatorFilho C.L.
dc.date2012
dc.date2015-06-26T20:29:37Z
dc.date2015-11-26T14:26:11Z
dc.date2015-06-26T20:29:37Z
dc.date2015-11-26T14:26:11Z
dc.date.accessioned2018-03-28T21:29:09Z
dc.date.available2018-03-28T21:29:09Z
dc.identifier9783642326387
dc.identifierLecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 7435 LNCS, n. , p. 226 - 236, 2012.
dc.identifier3029743
dc.identifier10.1007/978-3-642-32639-4_28
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84865035701&partnerID=40&md5=d415fd8e07db773a9cc81436eae56f42
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/97106
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/97106
dc.identifier2-s2.0-84865035701
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1246008
dc.descriptionThe prediction of seasonal streamflow series is very important in countries where power generation is predominantly done by hydroelectric plants. Echo state networks can be safely regarded as promising tools in forecasting because they are recurrent networks that have a simple and efficient training process based on linear regression. Recently, Boccato et al. proposed a new architecture in which the output layer is built using a principal component analysis and a Volterra filter. This work performs a comparative investigation between the performances of different ESNs in the context of the forecasting of seasonal streamflow series associated with Brazilian hydroelectric plants. Two possible reservoir design approaches were tested with the classical and the Volterra-based output layer structures, and a multilayer perceptron was also included to establish bases for comparison. The obtained results show the relevance of these networks and also contribute to a better understanding of their applicability to forecasting problems. © 2012 Springer-Verlag.
dc.description7435 LNCS
dc.description
dc.description226
dc.description236
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dc.descriptionSiqueira, H.V., Boccato, L., Attux, R., Lyra Filho, C., Seasonal Streamflow Series Forecasting Using Echo State Networks 10th Brazilian Congress on Computational Intelligence, Fortaleza-CE, Brazil (2011) (In Portuguese), , Previsão de séries de vazões com redes neurais de estados de eco
dc.descriptionONS - Electric System National Operator - Brazil (Online), , http://www.ons.org.br/operacao/vazoes_naturais.aspx
dc.languageen
dc.publisher
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsfechado
dc.sourceScopus
dc.titleEcho State Networks For Seasonal Streamflow Series Forecasting
dc.typeActas de congresos


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