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European Conference on Case-Based Reasoning

ECCBR 2002: Advances in Case-Based Reasoning pp 519–533Cite as

An Automated Hybrid CBR System for Forecasting

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2416))

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.

This research was supported in part by PGIDT00MAR30104PR project of Xunta de Galicia, Spain

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Author information

Authors and Affiliations

  1. Dpto. de Informática, E.S.E.I., University of Vigo, Campus Universitario As Lagoas s/n., 32004, Ourense, Spain

    Florentino Fdez-Riverola

  2. Dpto. de Informática y Automática, University of Salamanca, Facultad de Ciencias, Plaza de la Merced, s/n., 37008, Salamanca, Spain

    Juan M. Corchado

  3. Dpto. de Física Aplicada, University of Vigo, Facultad de Ciencias, Lagoas Marcosende, 36200, Vigo, Spain

    Jesús M. Torres

Authors
  1. Florentino Fdez-Riverola
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  2. Juan M. Corchado
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  3. Jesús M. Torres
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Editor information

Editors and Affiliations

  1. School of Computing, The Robert Gordon University, St. Andrew Street, Aberdeen, AB25 1HG, Scotland, UK

    Susan Craw

  2. Computing Science Department, University of Aberdeen, Aberdeen, AB24 3UE, Scotland, UK

    Alun Preece

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© 2002 Springer-Verlag Berlin Heidelberg

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Fdez-Riverola, F., Corchado, J.M., Torres, J.M. (2002). An Automated Hybrid CBR System for Forecasting. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_38

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  • DOI: https://doi.org/10.1007/3-540-46119-1_38

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44109-0

  • Online ISBN: 978-3-540-46119-7

  • eBook Packages: Springer Book Archive

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