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ALM: A Methodology for Designing Accurate Linguistic Models for Intelligent Data Analysis

  • Oscar Cordón
  • Francisco Herrera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)

Abstract

In this paper we introduce Accurate Linguistic Modelling, an approach to design linguistic models from data, which are accurate to a high degree and may be suitably interpreted. Linguistic models constitute an Intelligent Data Analysis structure that has the advantage of providing a human-readable description of the system modelled in the form of linguistic rules. Unfortunately, their accuracy is sometimes not as high as desired, thus causing the designer to discard them and replace them by other kinds of more accurate but less interpretable models. ALM has the aim of solving this problem by improving the accuracy of linguistic models while maintaining their descriptive power, taking as a base some modifications on the interpolative reasoning developed by the Fuzzy Rule-Based System composing the model. In this contribution we shall introduce the main aspects of ALM, along with a specific design process based on it. The behaviour of this learning process in the solving of two different applications will be shown.

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References

  1. 1.
    Bastian, A.: How to handle the flexibility of linguistic variables with applications. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2:4 (1994) 463–484.CrossRefMathSciNetGoogle Scholar
  2. 2.
    Cordón, O., Herrera, F., Peregrín, A.: Applicability of the fuzzy operators in the design of fuzzy logic controllers. Fuzzy Sets and Systems 86 (1997) 15–41.zbMATHCrossRefGoogle Scholar
  3. 3.
    Cordón, O., Herrera, F.: A three-stage evolutionary process for learning descriptive and approximative fuzzy logic controller knowledge bases from examples. International Journal of Approximate Reasoning 17:4 (1997) 369–407.zbMATHCrossRefGoogle Scholar
  4. 4.
    Cordón, O., Herrera, F.: A Proposal for Improving the Accuracy of Linguistic Modelling. Technical Report DECSAI-98113. Dept. of Computer Science and A.I. University of Granada. Spain (May, 1998).Google Scholar
  5. 5.
    Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989).Google Scholar
  6. 6.
    Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern Classification. Fuzzy Sets and Systems 52 (1992) 21–32.CrossRefGoogle Scholar
  7. 7.
    Mamdani, E. H., Applications of fuzzy algorithm for control a simple dynamic plant, Proceedings of the IEE, 121:12 (1974) 1585–1588.Google Scholar
  8. 8.
    Nauck, D., Klawonn, F., Kruse, R.: Fundations of Neuro-Fuzzy Systems. John Willey & Sons (1997).Google Scholar
  9. 9.
    Nozaki, K., Ishibuchi, H., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86 (1997) 251–270.CrossRefGoogle Scholar
  10. 10.
    Pedrycz, W. (Ed.): Fuzzy Modelling: Paradigms and Practice. Kluwer Academic Press (1996).Google Scholar
  11. 11.
    Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modelling. IEEE Transactions on Fuzzy Systems 1:1 (1993) 7–31.CrossRefGoogle Scholar
  12. 12.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics 15:1 (1985) 116–132.zbMATHGoogle Scholar
  13. 13.
    Wang, L. X., Mendel, J. M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics 22 (1992) 1414–1427.CrossRefMathSciNetGoogle Scholar
  14. 14.
    Zadeh, L. A.: Fuzzy sets. Information and Control 8 (1965) 338–353.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Oscar Cordón
    • 1
  • Francisco Herrera
    • 1
  1. 1.Dept. of Computer Science and Artificial IntelligenceE.T.S. de Ingeniería Informática, University of GranadaGranadaSpain

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