• DocumentCode
    3617508
  • Title

    Visual comparison of performance for different activation functions in MLP networks

  • Author

    F. Piekniewski;L. Rybicki

  • Author_Institution
    Fac. of Math. & Comput. Sci., Nicolaus Copernicus Univ., Torun, Poland
  • Volume
    4
  • fYear
    2004
  • fDate
    6/26/1905 12:00:00 AM
  • Firstpage
    2947
  • Abstract
    Multi layer perceptron networks have been successful in many applications, yet there are many unsolved problems in the theory. Commonly, sigmoidal activation functions have been used, giving good results. The backpropagation algorithm might work with any other activation function on one condition though - it has to have a differential. We investigate some possible activation functions and compare the results they give on some sample data sets.
  • Keywords
    "Intelligent networks","Transfer functions","Neurons","Neural networks","Mathematics","Computer science","Electronic mail","Logistics","Application software","Interpolation"
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-8359-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2004.1381133
  • Filename
    1381133