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dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorFyfe, Colin
dc.date.accessioned2017-09-05T11:02:35Z
dc.date.available2017-09-05T11:02:35Z
dc.date.issued1999
dc.identifier.citationArtificial Intelligence in Engineering. Volumen 13 (4), pp. 351-357. Elsevier BV.
dc.identifier.issn0954-1810 (Print)
dc.identifier.urihttp://hdl.handle.net/10366/134480
dc.description.abstractThis article presents the results of using a novel Negative Feedback Artificial Neural Network for extraction of models of the thermal structure of oceanographic water masses and to forecast time series in real time. The results obtained using this model are compared with those obtained using a Linear Regression and an ARIMA model. The article presents the Negative Feedback Artificial Neural Network, shows how it extracts the model behind the data set and discuses the Artificial Neural Network's forecasting abilities.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherElsevier BV
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleUnsupervised neural method for temperature forecasting
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
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