Relational Type-2 Interval Fuzzy Systems

  • Rafał Scherer
  • Janusz T. Starczewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6067)


In this paper we combine type-2 fuzzy logic with a relational fuzzy system paradigm. Incorporating type-2 fuzzy sets brings new possibilities for performance improvement. The relational fuzzy system scheme reduces significantly the number of system parameters to be trained. Numerical simulations of the proposed combination of systems are presented.


Membership Function Fuzzy System Fuzzy Membership Function Fuzzy Logic System Certainty Factor 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Castillo, O., Aguilar, L., Cazarez-Castro, N., Boucherit, M.: Application of type-2 fuzzy logic controller to an induction motor drive with seven-level diode-clamped inverter and controlled infeed. Electrical Engineering 90(5), 347–359 (2008)CrossRefGoogle Scholar
  2. 2.
    Castillo, O., Melin, P.: Intelligent systems with interval type-2 fuzzy logic. International Journal of Innovative Computing, Information and Control 4(4), 771–783 (2008)Google Scholar
  3. 3.
    Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous robots. IEEE Transactions on Fuzzy Systems 12(4), 524–539 (2004)CrossRefGoogle Scholar
  4. 4.
    Mendez, G.: Interval type-2 anfis. In: Advances in Soft Computing – Innovations in Hybrid Intelligent Systems, pp. 64–71 (2007)Google Scholar
  5. 5.
    Torres, P., Sez, D.: Type-2 fuzzy logic identification applied to the modeling of a robot hand. In: Proc. FUZZ-IEEE 2008, Hong Kong (June 2008)Google Scholar
  6. 6.
    Uncu, O., Turksen, I.: Discrete interval type 2 fuzzy system models using uncertainty in learning parameters. IEEE Transactions on Fuzzy Systems 15(1), 90–106 (2007)CrossRefGoogle Scholar
  7. 7.
    Starczewski, J.: Efficient triangular type-2 fuzzy logic systems. International Journal of Approximate Reasoning 50, 799–811 (2009)zbMATHCrossRefGoogle Scholar
  8. 8.
    Nowicki, R.: Rough-neuro-fuzzy structures for classification with missing data. IEEE Transactions on Systems, Man, and Cybernetics — Part B: Cybernetics 39(6), 1334–1347 (2009)CrossRefGoogle Scholar
  9. 9.
    Scherer, R., Rutkowski, L.: Neuro-fuzzy relational systems. In: Proc. 2002 International Conference on Fuzzy Systems and Knowledge Discovery, Singapore, pp. 18–22 (November 2002)Google Scholar
  10. 10.
    Scherer, R., Rutkowski, L.: Neuro-fuzzy relational classifiers. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 376–380. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Setness, M., Babuska, R.: Fuzzy relational classifier trained by fuzzy clustering. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 29(5), 619–625 (1999)CrossRefGoogle Scholar
  12. 12.
    Liang, Q., Mendel, J.: Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems 8, 535–550 (2000)CrossRefGoogle Scholar
  13. 13.
    Karnik, N., Mendel, J., Liang, Q.: Type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems 7(6), 643–658 (1999)CrossRefGoogle Scholar
  14. 14.
    Karnik, N., Mendel, J.: Centroid of a type-2 fuzzy set. Information Sciences 132, 195–220 (2001)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rafał Scherer
    • 1
  • Janusz T. Starczewski
    • 1
  1. 1.Department of Computer EngineeringCzȩstochowa University of TechnologyCzȩstochowaPoland

Personalised recommendations