Affiliations: Dpto. de Informática, E.S.E.I., University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n., 32004, Ourense, Spain E‐mail: riverola@uvigo.es | Dpto. de Informática y Automática, Facultad de Ciencias, University of Salamanca, Plaza de la Merced,s/n., 37008, Salamanca, Spain E‐mail: corchado@gugu.usal.es
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 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 in a different stage of the reasoning cycle of the case‐based reasoning system to retrieve, adapt and review the proposed solution to the present 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 experiments are presented.
Keywords: Hybrid forecasting system, neural indexing and adaptation, fuzzy revision, extracting fuzzy rules, red tides
