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

Simple item page
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.accessioned2010-12-27T12:30:13Z
dc.date.accessioned2010-12-29T09:04:02Z
dc.date.available2010-12-29T09:04:02Z
dc.date.issued2007
dc.descriptionProceeding of: International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2007 (CAEPIA 2007). 12-13 November, 2007, Salamanca, Spain.
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.
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationInnovations in hybrid intelligent systems. Springer, 2007, pp. 231-238
dc.identifier.doi10.1007/978-3-540-74972-1_31
dc.identifier.isbn978-3-540-74971-4
dc.identifier.issn1615-3871 (print)
dc.identifier.issn1860-0794 (online)
dc.identifier.publicationfirstpage231
dc.identifier.publicationlastpage238
dc.identifier.publicationtitleInnovations in hybrid intelligent systems
dc.identifier.publicationvolume44
dc.identifier.urihttps://hdl.handle.net/10016/9889
dc.language.isoeng
dc.publisherSpringer
dc.relation.eventdate12-13 November, 2007
dc.relation.eventplaceSalamanca (Spain)
dc.relation.eventtitleInternational Workshop on Hybrid Artificial Intelligence Systems, HAIS 2007 (CAEPIA 2007)
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
dc.rights.accessRightsopen access
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
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
design_Innovation_2007.pdf
Size:
300.05 KB
Format:
Adobe Portable Document Format
Download
Collections
DI - CAOS - Comunicaciones en Congresos y otros eventos
DI - CAOS - Capítulos de Monografías