• DocumentCode
    2962804
  • Title

    Fuzzy Adaptive Resonance Theory Combining Overlapped Category in consideration of connections

  • Author

    Isawa, Haruka ; Matsushita, Haruna ; Nishio, Yoshifumi

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Tokushima Univ., Tokushima
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    3595
  • Lastpage
    3600
  • Abstract
    Adaptive resonance theory (ART) is an unsupervised neural network. Fuzzy ART (FART) is a variation of ART, allows both binary and continuous input patterns. However, fuzzy ART has the category proliferation problem. In this study, to solve this problem, we propose a new fuzzy ART algorithm: fuzzy ART combining overlapped category in consideration of connections (C-FART). C-FART has two important features. One is to make connections between similar categories. The other is to combine overlapping categories into with connections one category. We investigate the behavior of C-FART, and compare C-FART with the conventional FART.
  • Keywords
    adaptive resonance theory; fuzzy neural nets; fuzzy adaptive resonance theory; overlapped category; unsupervised neural network; Adaptive systems; Fuzzy logic; Humans; Neural networks; Neurons; Pattern recognition; Predictive models; Resonance; Subspace constraints; Supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4634312
  • Filename
    4634312