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A Hybrid CBR Model for Forecasting in Complex Domains
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A Hybrid CBR Model for Forecasting in Complex Domains
Florentino Fdez-Riverola3 and Juan M. Corchado4 
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Dpto. de Informática, E.S.E.I., University of Vigo, Campus Universitario As Lagoas s/n., 32004 Ourense, Spain |
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Dpto. de Informática y Automática, University of Salamanca Facultad de Ciencias, Plaza de la Merced, s/n., 37008 Salamanca, Spain |
Abstract
A 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. The proposed model
employs a case-based reasoning system 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 in a different stage of the reasoning
cycle of the case-based reasoning system to retrieve, to adapt and to review the proposed solution to the problem. 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 those experiments are presented.
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