• DocumentCode
    353277
  • Title

    Justification of a neuron-adaptive activation function

  • Author

    Xu, Shuxiang ; Zhang, Ming

  • Author_Institution
    Sch. of Comput., Tasmania Univ., Hobart, Tas., Australia
  • Volume
    3
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    465
  • Abstract
    An empirical justification of a neuron-adaptive activation function for feedforward neural networks has been proposed in this paper. Simulation results reveal that feedforward neural networks with the proposed neuron-adaptive activation function present several advantages over traditional neuron-fixed feedforward networks such as increased flexibility, much reduced network size, faster learning, and lessened approximation errors
  • Keywords
    feedforward neural nets; transfer functions; approximation errors; feedforward neural networks; flexibility; learning; neuron-adaptive activation function; Approximation error; Computational modeling; Computer networks; Electronic mail; Feedforward neural networks; Feedforward systems; Function approximation; Neural networks; Neurons; Spline;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
  • Conference_Location
    Como
  • ISSN
    1098-7576
  • Print_ISBN
    0-7695-0619-4
  • Type

    conf

  • DOI
    10.1109/IJCNN.2000.861351
  • Filename
    861351
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=353277