Skip to main content
Book cover

Ibero-American Conference on Artificial Intelligence

IBERAMIA 2002: Advances in Artificial Intelligence — IBERAMIA 2002 pp 101–110Cite as

A Hybrid CBR Model for Forecasting in Complex Domains

  • Conference paper

    • We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

  • First Online:

  • 719 Accesses

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/3-540-36131-6_11
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   139.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-36131-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   179.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Corchado, J. M., Lees, B.: A Hybrid Case-based Model for Forecasting. Applied Artificial Intelligence, 15, num. 2, (2001) 105–127

    CrossRef  Google Scholar 

  2. Corchado, J. M., Lees, B., Aiken, J.: Hybrid Instance-based System for Predicting Ocean Temperatures. International Journal of Computational Intelligence and Applications, 1, num. 1, (2001) 35–52

    CrossRef  Google Scholar 

  3. Corchado, J. M., Aiken, J., Rees, N.: Artificial Intelligence Models for Oceanographic Forecasting. Plymouth Marine Laboratory, UK, (2001)

    Google Scholar 

  4. Fritzke, B.: Growing Self-Organizing Networks-Why?. In Verleysen, M. (Ed.). European Symposium on Artificial Neural Networks, ESANN-96. Brussels, (1996) 61–72

    Google Scholar 

  5. Fritzke, B.: Fast learning with incremental RBF Networks. Neural Processing Letters, 1, num. 1, (1994) 2–5

    CrossRef  Google Scholar 

  6. Jin, Y., Seelen, W. von., and Sendho., B.: Extracting Interpretable Fuzzy Rules from RBF Neural Networks. Internal Report IRINI 00-02, Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany, (2000)

    Google Scholar 

  7. Azuaje, F., Dubitzky, W., Black, N., and Adamson, K.: Discovering Relevance Knowledge in Data: A Growing Cell Structures Approach. IEEE Transactions on Systems, Man and Cybernetics, 30, (2000) 448–460

    CrossRef  Google Scholar 

  8. Corchado, J. M., and Lees, B.: Adaptation of Cases for Case-based Forecasting with Neural Network Support. In Pal, S. K., Dilon, T. S., and Yeung, D. S. (Eds.). Soft Computing in Case Based Reasoning. London: Springer Verlag, (2000) 293–319

    Google Scholar 

  9. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, (1985) 116–132

    MATH  Google Scholar 

  10. Tomczak, M., Godfrey, J. S.: Regional Oceanographic: An Introduction. Pergamon, New York, (1994)

    Google Scholar 

Download references

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

Authors
  1. Florentino Fdez-Riverola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan M. Corchado
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Telefónica Investigación y Desarrollo, Emilio Vargas 6, 28043, Madrid, Spain

    Francisco J. Garijo

  2. Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, ETS Ingeniería Informática, 41012, Seville, Spain

    José C. Riquelme & Miguel Toro & 

Rights and permissions

Reprints and Permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

  • eBook Packages: Springer Book Archive

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.