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Evolutionary Learning for Neuro-fuzzy Ensembles with Generalized Parametric Triangular Norms

  • Marcin Gabryel
  • Marcin Korytkowski
  • Agata Pokropinska
  • Rafał Scherer
  • Stanisław Drozda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6113)

Abstract

In this paper we present a method for designing neuro-fuzzy systems with Mamdani-type inference and parametric t-norm connecting rule antecedents. Hamacher product was used as t-norm. The neuro-fuzzy systems are used to create an ensemble of classifiers. After obtaining the ensemble by bagging, every neuro-fuzzy system has its t-norm parameters fine-tuned. Thanks to this the accuracy is improved and the number of parameters can be reduced. The proposed method is tested on a well known benchmark.

Keywords

Fuzzy System Ensemble Method Evolutionary Learn Triangular Norm Genetic Fuzzy System 
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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References

  1. 1.
    Breiman, L.: Bagging predictors. Machine Learning 26(2), 123–140 (1996)Google Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  5. 5.
    Gabryel, M., Cpalka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: I Workshop on Genetic Fuzzy Systems, Granada, pp. 119–123 (2005)Google Scholar
  6. 6.
    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
  7. 7.
    Gabryel, M., Rutkowski, L.: Evolutionary Methods for Designing Neuro-fuzzy Modular Systems Combined by Bagging Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 398–404. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Kuncheva, L.I.: Fuzzy Classifier Design. Physica Verlag, Heidelberg (2000)zbMATHGoogle Scholar
  10. 10.
    Klement, E.P., Mesiar, R., Pap, E.: Triangular norms. Position paper II: general constructions and parametrized families. Fuzzy Sets and Systems 145, 411–438 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)zbMATHGoogle Scholar
  12. 12.
    Rutkowska, D., Nowicki, R.: Implication-Based Neuro-Fuzzy Architectures. Intenrational Journal of Applied Mathematics and Computer Science 10(4) (2000)Google Scholar
  13. 13.
    Rutkowska, D.: Neuro Fuzzy Architectures and Hybrid Learning. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  14. 14.
    Rutkowski, L.: Computational Inteligence, Methods and Techniques. Springer, Heidelberg (2008)Google Scholar
  15. 15.
    Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers, Dordrecht (2004)zbMATHGoogle Scholar
  16. 16.
    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
  • Marcin Korytkowski
    • 1
    • 2
  • Agata Pokropinska
    • 4
  • Rafał Scherer
    • 1
    • 5
  • Stanisław Drozda
    • 3
  1. 1.Department of Computer EngineeringCzȩstochowa University of TechnologyCzȩstochowaPoland
  2. 2.The Professor Kotarbinski Olsztyn Academy of Computer Science and ManagementOlsztynPoland
  3. 3.The Faculty of Mathematics and Computer SciencesUniversity of Warmia and Mazury in OlsztynOlsztynPoland
  4. 4.Institute of Mathematics and Computer ScienceJan Długosz UniversityCzȩstochowaPoland
  5. 5.Institute of Information TechnologyAcademy of Management (SWSPiZ)ŁódźPoland

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