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Modular Type-2 Neuro-fuzzy Systems

  • Janusz Starczewski
  • Rafał Scherer
  • Marcin Korytkowski
  • Robert Nowicki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4967)

Abstract

In the paper we study a modular system which can be converted into a type-2 neuro-fuzzy system. The rule base of such system consists of triangular type-2 fuzzy sets. The modular structure is trained using the backpropagation method combined with the AdaBoost algorithm. By applying the type-2 neuro-fuzzy system, the modular structure is converted into a compressed form. This allows to overcome the training problem of type-2 neuro-fuzzy systems. An illustrative example is given to show the efficiency of our approach in the problems of classification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Janusz Starczewski
    • 1
    • 2
  • Rafał Scherer
    • 1
    • 2
  • Marcin Korytkowski
    • 1
    • 3
  • Robert Nowicki
    • 1
    • 2
  1. 1.Department of Computer EngineeringCzȩstochowa University of TechnologyCzȩstochowaPoland
  2. 2.Department of Artificial IntelligenceAcademy of Humanities and Economics in LodzŁódźPoland
  3. 3.Olsztyn Academy of Computer Science and ManagementOlsztynPoland

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