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Design of artificial neural networks based on genetic algorithms to forecast time series

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorPeralta, Juan
dc.contributor.authorGutiérrez Sánchez, Germán
dc.contributor.authorSanchis de Miguel, María Araceli
dc.date.accessioned2010-12-09T09:16:36Z
dc.date.available2010-12-09T09:16:36Z
dc.date.issued2007
dc.description.abstractIn this work an initial approach to design Artificial Neural Networks to forecast time series is tackle, and the automatic process to design is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks for NN3 Forecasting Time Series Competition are shown.
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dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationInnovations in hybrid intelligent systems, 2007, p. 231-238
dc.identifier.doi10.1007/978-3-540-74972-1_31
dc.identifier.isbn978-3-540-74971-4
dc.identifier.issn1615-3871 (impreso)
dc.identifier.issn1860-0794 (online)
dc.identifier.publicationfirstpage231
dc.identifier.publicationlastpage238
dc.identifier.publicationtitleInnovations in hybrid intelligent systems
dc.identifier.urihttps://hdl.handle.net/10016/9783
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesAdvances in Soft Computing, vol. 44
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-540-74972-1_31
dc.rights© Springer-Verlag Berlin Heidelberg
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformática
dc.subject.otherTime series forecasting
dc.subject.otherArtificial neural networks desing
dc.subject.otherGenetic algorithms
dc.subject.otherDirect encoding scheme
dc.titleDesign of artificial neural networks based on genetic algorithms to forecast time series
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dspace.entity.typePublication
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