Employing TSK Fuzzy Models to Automate the Revision Stage of a CBR System

  • Florentino Fernández-Riverola
  • Juan M. Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3040)


CBR systems are normally used to assist experts in the resolution of problems. During the last few years, researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a fuzzy logic based method that automates the review stage of case-based reasoning systems and aids in the process of obtaining an accurate solution. The proposed methodology has been derived as an extension of the Sugeno Fuzzy model, and evaluates different solutions by reviewing their score in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of a biological forecasting system and to improve its performance.


Fuzzy System Fuzzy Rule Fuzzy Model Fuzzy Rule Base Takagi Sugeno Kang 
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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  1. 1.
    Watson, I.: Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Francisco (1997)zbMATHGoogle Scholar
  2. 2.
    Pal, S.K., Dillon, T.S., Yeung, D.S.: Soft Computing in Case Based Reasoning. Springer, London (2000)Google Scholar
  3. 3.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR Systems using Kernel Methods. International Journal of Intelligent Systems 16(4) (2001)Google Scholar
  4. 4.
    Fdez-Riverola, F., Corchado, J.M.: FSfRT: Forecasting System for Red Tides. Applied Intelligence. Soft Computing in Case-Based Reasoning (2003) (in press)Google Scholar
  5. 5.
    Corchado, J.M., Aiken, J.: Hybrid Artificial Intelligence Methods in Oceanographic Forecasting Models. IEEE SMC Transactions Part C (2003)Google Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy Sets. Inf. Contr. 8, 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Jaczynski, M., Trousse, B.: Fuzzy logic for the retrieval step of a case-based reasoner. In: Haton, J.-P., Manago, M., Keane, M.A. (eds.) EWCBR 1994. LNCS, vol. 984, pp. 313–321. Springer, Heidelberg (1995)Google Scholar
  8. 8.
    Plaza, E., de Mántaras, L.: A case-based apprentice that learns from fuzzy examples. Metholologies for Intelligent Systems 5, 420–427 (1990)Google Scholar
  9. 9.
    Dutta, S., Bonissone, P.P.: Integrating case rule-based reasoning. Int. J. of Approximate Reasoning 8, 163–203 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Plaza, E., de Mántaras, L., Armengol, E.: On the importance of similitude: an entropybased assessment. Report IIIA 96/14, IIIA-CSIC, University of Barcelona, Bellaterra, Spain (1996)Google Scholar
  11. 11.
    Mandani, E.H.: Applications of fuzzy algorithms for control of a simple dynamic plant. Proc. Inst. Electr. Eng. 121, 1585–1588 (1974)CrossRefGoogle Scholar
  12. 12.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15, 116–132 (1985)zbMATHGoogle Scholar
  13. 13.
    Setnes, M., Babuška, R., Kaymak, U., Lemke, R.: Similarity measures in fuzzy rule base simplification. IEEE Transactions on Systems, Man and Cybernetics 28, 376–386 (1998)CrossRefGoogle Scholar
  14. 14.
    Fritzke, B.: Fast Learning with Incremental RBF Networks. Neural Processing Letters 1(1), 2–5 (1994)CrossRefGoogle Scholar
  15. 15.
    Jin, Y., von Seelen, W., Sendhoff, B.: Extracting Interpretable Fuzzy Rules from RBF Neural Networks. Internal Report IRINI 00-02, Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany (2000)Google Scholar
  16. 16.
    Fdez-Riverola, F.: Neuro-symbolic model for unsupervised forecasting of changing environments, Ph.D. Dissertation, Dept. Informática, University of Vigo (2002)Google Scholar
  17. 17.
    Fritzke, B.: Growing Self-Organizing Networks-Why? In: Proc. European Symposium on Artificial Neural Networks, pp. 61–72 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Florentino Fernández-Riverola
    • 1
  • Juan M. Corchado
    • 2
  1. 1.Dept. Informática.University of VigoOurenseSpain
  2. 2.Dept. de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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