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dc.contributor.author | Corchado Rodríguez, Juan Manuel | |
dc.contributor.author | Aiken, Jim | |
dc.date.accessioned | 2017-09-05T11:02:28Z | |
dc.date.available | 2017-09-05T11:02:28Z | |
dc.date.issued | 2002 | |
dc.identifier.citation | IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). Volumen 32 (4), pp. 307-313. Institute of Electrical & Electronics Engineers (IEEE). | |
dc.identifier.issn | 1094-6977 (Print) | |
dc.identifier.uri | http://hdl.handle.net/10366/134466 | |
dc.description.abstract | An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid artificial intelligence model can provide a more effective means of performing such predictions than either connectionist or symbolic techniques used separately. The hybrid forecasting system that has been developed consists of a case-based reasoning system integrated with a radial basis function artificial neural network. The results obtained from experiments in which the system operated in real time in the oceanographic environment, are presented | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical & Electronics Engineers (IEEE) | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ | |
dc.subject | Computer Science | |
dc.title | Hybrid artificial intelligence methods in oceanographic forecast models | |
dc.type | info:eu-repo/semantics/article | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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