• DocumentCode
    1818244
  • Title

    Fourier neural networks

  • Author

    Silvescu, Adrian

  • Author_Institution
    Dept. of Comput. Sci., Iowa State Univ., Ames, IA, USA
  • Volume
    1
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    488
  • Abstract
    A new kind of neuron model that has a Fourier-like in/out function is introduced. The model is discussed in a general theoretical framework and some completeness theorems are presented. Current experimental results show that the new model outperforms, by a large margin both in representational power and convergence speed, the classical mathematical model of neuron based on weighted sum of inputs filtered by a nonlinear function. The new model is also appealing from a neurophysiological point of view because it produces a more realistic representation by considering the inputs as oscillations
  • Keywords
    convergence; neural nets; neurophysiology; physiological models; Fourier neural networks; completeness theorems; convergence; neuron model; neurophysiology; nonlinear function; weighted sum; Artificial intelligence; Artificial neural networks; Computational modeling; Computer networks; Computer science; Convergence; Intelligent networks; Mathematical model; Neural networks; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.831544
  • Filename
    831544