• DocumentCode
    3700082
  • Title

    Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms

  • Author

    Yevgeniy Bodyanskiy;Galina Setlak;Dmytro Peleshko;Olena Vynokurova

  • Author_Institution
    Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, Lenina av. 14, Kharkiv, Ukraine
  • Volume
    1
  • fYear
    2015
  • Firstpage
    328
  • Lastpage
    333
  • Abstract
    In this paper we propose architecture of hybrid generalized additive neuro-fuzzy system. Such system is hybrid of the neuro-fuzzy system of Wang-Mendel and the generalized additive models of Hastie-Tibshirani. Proposed hybrid generalized additive neuro-fuzzy system can be used for solving different tasks of computational intelligence and data stream mining. The results of experimental modelling confirm the effectiveness and computational simplicity of the proposed approach in comparison with conventional neuro-fuzzy systems.
  • Keywords
    "Additives","Neurons","Data mining","Tuning","Computer architecture","Fuzzy systems","Approximation methods"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015 IEEE 8th International Conference on
  • Print_ISBN
    978-1-4673-8359-2
  • Type

    conf

  • DOI
    10.1109/IDAACS.2015.7340753
  • Filename
    7340753