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)


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.


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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© 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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