Advertisement

Evolutionary Designing of Logic-Type Fuzzy Systems

  • Marcin Gabryel
  • Leszek Rutkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

In this paper we present a method for designing the logic-type fuzzy system. In this kind of fuzzy systems antecedents and consequences, in the individual rules, are connected by a fuzzy implication. In our method, the whole system is designed by an evolutionary algorithm, including learning of parameters of membership functions and selection of an appopriate fuzzy implication and triangular norms. The results of simulations illustrate efficiency of our method.

Keywords

Membership Function Fuzzy System Individual Rule Triangular Norm Fuzzy Implication 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cordon, O., Herrera, F., Hoffman, F., Magdalena, L.: Genetic Fuzzy System. Evolutionary Tunning and Learning of Fuzzy Knowledge Bases. World Scientific, Singapore (2000)Google Scholar
  2. 2.
    Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy sets and systems 141, 5–31 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  4. 4.
    Gabryel, M., Cpalka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: I Workshop on Genetic Fuzzy Systems, Genewa, pp. 119–123 (2005)Google Scholar
  5. 5.
    Gabryel, M., Rutkowski, L.: Evolutionary Learning of Mamdani-type Neuro-Fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 354–359. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Korytkowski, M., Gabryel, M., Rutkowski, L., Drozda, S.: Evolutionary Methods to Create Interpretable Modular System. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 405–413. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)zbMATHGoogle Scholar
  8. 8.
    Rutkowska, D., Nowicki, R.: Implication-Based Neuro-Fuzzy Architectures. Intenrational Journal of Applied Mathematics and Computer Science 10(4) (2000)Google Scholar
  9. 9.
    Rutkowska, D.: Neuro Fuzzy Architectures and Hybrid Learning. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  10. 10.
    Rutkowski, L.: Computational Inteligence. Methods and Techniques. Springer, Heidelberg (2008)Google Scholar
  11. 11.
    Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers, Dordrecht (2004)zbMATHGoogle Scholar
  12. 12.
    Mertz, C.J., Murphy, P.M.: UCI respository of machine learning databases, http://www.ics.uci.edu/pub/machine-learning-databases

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcin Gabryel
    • 1
    • 2
  • Leszek Rutkowski
    • 3
    • 1
  1. 1.Department of Computer EngineeringCzȩstochowa University of TechnologyCzȩstochowaPoland
  2. 2.The Professor Kotarbinski Olsztyn Academy of Computer Science and ManagementOlsztynPoland
  3. 3.Institute of Information TechnologyAcademy of Management (SWSPiZ)LodzPoland

Personalised recommendations