An Automated Hybrid CBR System
for Forecasting *

Florentino Fdez-Riverola1, Juan M. Corchado2, and Jess M. Torres3

1 Dpto. de Informtica, E.S.E.I., University of Vigo,
Campus Universitario As Lagoas s/n., 32004, Ourense, Spain
riverola@uvigo.es
2 Dpto. de Informtica y Automtica, University of Salamanca,
Facultad de Ciencias, Plaza de la Merced, s/n., 37008, Salamanca, Spain
corchado@usal.es
3 Dpto. de Fsica Aplicada, University of Vigo,
Facultad de Ciencias, Lagoas Marcosende, 36200, Vigo, Spain
jesu@uvigo.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 proposed system employs a case-based reasoning
model that incorporates 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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