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A Hybrid CBR Model for Forecasting in Complex Domains

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 2527)

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.

Keywords

  • Radial Basis Function
  • Fuzzy System
  • Iberian Peninsula
  • Radial Basis Function Neural Network
  • Radial Basis Function Network

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2002 Springer-Verlag Berlin Heidelberg

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Fdez-Riverola, F., Corchado, J.M. (2002). A Hybrid CBR Model for Forecasting in Complex Domains. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_11

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  • DOI: https://doi.org/10.1007/3-540-36131-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

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