Fuzzy neural networks are a powerful machine learning technique, that can be used in a large number of applications. Proper learning of fuzzy neural networks requires a lot of computational effort and the fuzzy-rule designs of these networks suffer from the curse of dimensionality. To alleviate these problems, a simplified fuzzy neural network is presented. The proposed simplified network model can be efficiently initialized with considerably high predictive power. We propose the ensembling approach, thus, using the new simplified neural network models as the type of a general-purpose fuzzy base-learner. The new base-learner properties are analyzed and the practical results of the new algorithm are presented on the robotic hand controller application.


neural network gradient boosting fuzzy neural network neural ensemble boosting robotic control machine learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexey Natekin
    • 1
  • Alois Knoll
    • 2
  1. 1.Fortiss GmbHMunichGermany
  2. 2.Technical University MunichGarchingGermany

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