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dc.contributor.authorFernández Riverola, Florentino
dc.contributor.authorCorchado Rodríguez, Juan Manuel
dc.identifier.citationApplied Intelligence. Volumen 21 (3), pp. 251-264. Springer Science + Business Media.
dc.identifier.issn0924-669X (Print) / 1573-7497 (Online)
dc.description.abstractA hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used at a different stage of the reasoning cycle of the case-based reasoning system to retrieve historical data, to adapt it to the present problem and to review the proposed solution. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments, in which the system operated in a real environment, are presented.
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectComputer Science
dc.titleFSfRT: Forecasting System for Red Tides

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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported