Modular Neuro-Fuzzy Systems Based on Generalized Parametric Triangular Norms

  • Marcin Korytkowski
  • Rafał Scherer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6067)


Neuro-fuzzy systems are used for modeling and classification thanks to their advantages such as interpretable knowledge and ability to learn from data. They are similar to neural networks but their design is structured depending on knowledge in the form of fuzzy rules. Final decision is inferred by fuzzy inference using triangular norms. The paper presents modular neuro-fuzzy systems based on parametric classes of generalized conjunction and disjunction operations. The proposed systems are better adjustable to data during learning. Part of this flexibility is achieved by the new operators what do not alter the original knowledge of fuzzy rules. The method described in the paper is tested on several known benchmarks.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), Google Scholar
  2. 2.
    Batyrshin, I., Kaynak, O., Rudas, I.: Fuzzy Modeling Based on Generalized Conjunction Operations. IEEE Transactions on Fuzzy Systems 10(5), 678–683 (2002)CrossRefGoogle Scholar
  3. 3.
    Batyrshin, I., Kaynak, O.: Parametric Classes of Generalized Conjunction and Disjunction Operations for Fuzzy Modeling. IEEE Transactions on Fuzzy Systems 7(5), 586–596 (1999)CrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Bagging predictors. Machine Learning 26(2), 123–140 (1996)Google Scholar
  5. 5.
    Jang, R.J.-S., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River (1997)Google Scholar
  6. 6.
    Klement, E.P., Mesiar, R., Pap, E.: Triangular norms. Position paper II: general constructions and parameterized families. Fuzzy Sets and Systems 145, 411–438 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. John Wiley & Sons, Chichester (2004)zbMATHCrossRefGoogle Scholar
  8. 8.
    Nauck, D., Klawon, F., Kruse, R.: Foundations of Neuro - Fuzzy Systems. John Wiley, Chichester (1997)Google Scholar
  9. 9.
    Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers, Boston (2004)zbMATHGoogle Scholar
  10. 10.
    Schapire, R.E.: A brief introduction to boosting. In: Sixteenth International Joint Conference on Artificial Intelligence, pp. 1401–1406 (1999)Google Scholar
  11. 11.
    Wang, L.-X.: Adaptive Fuzzy Systems And Control. PTR Prentice Hall, Englewood Cliffs (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcin Korytkowski
    • 1
    • 2
  • Rafał Scherer
    • 1
  1. 1.Department of Computer EngineeringCzȩstochowa University of TechnologyCzȩstochowaPoland
  2. 2.Olsztyn Academy of Computer Science and ManagementOlsztynPoland

Personalised recommendations