• DocumentCode
    2867811
  • Title

    Improved Fuzzy Single Layer Supervised Learning Algorithm

  • Author

    Jong-Chan Kim ; Kyeong-Jin Ban ; Eung-kon Kim ; Yang-sun Lee ; An-Suk Oh

  • Author_Institution
    Dept. of Comput. Eng., Sunchon Nat. Univ., Sunchon, South Korea
  • fYear
    2011
  • fDate
    28-30 June 2011
  • Firstpage
    44
  • Lastpage
    47
  • Abstract
    In this paper, we improve the convergence prevented from vibrating decision boundary with bias term and suggest a linear activation function. We propose an enhanced fuzzy single layer perceptron which reduces the learning time introducing the rate of learning and the concept of momentum. We applied to Exclusive OR problem and pattern recognition of letters to analyze the performance of learning through enhanced fuzzy single layer perceptron and precedent fuzzy single layer perceptron. After the number of epoch and the convergence of enhanced fuzzy single layer perceptron were compared with those of precedent one, we found that enhanced one had far less time for learning and improved the convergence.
  • Keywords
    fuzzy set theory; learning (artificial intelligence); pattern recognition; perceptrons; bias term; exclusive OR problem; linear activation function; pattern recognition; precedent fuzzy single layer perceptron enhancement; supervised learning algorithm; Artificial neural networks; Classification algorithms; Convergence; Fuzzy logic; Learning systems; Pattern classification; Pattern recognition; bias term; fuzzy function; fuzzy single layer perceptron; learning rate; momentum;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Ubiquitous Engineering (MUE), 2011 5th FTRA International Conference on
  • Conference_Location
    Loutraki
  • Print_ISBN
    978-1-4577-1228-9
  • Electronic_ISBN
    978-0-7695-4470-0
  • Type

    conf

  • DOI
    10.1109/MUE.2011.19
  • Filename
    5992169