• DocumentCode
    2595838
  • Title

    Gaussian perceptron: experimental results

  • Author

    Kwon, Taek Mu

  • Author_Institution
    Dept. of Comput. Eng., Minnesota Univ., Duluth, MN, USA
  • fYear
    1991
  • fDate
    13-16 Oct 1991
  • Firstpage
    1593
  • Abstract
    A new neural model which has a Gaussian activation function is presented. This model is referred to as the Gaussian perceptron. For the training of single-layered Gaussian perceptrons, the Gaussian perceptron learning algorithm, which is a variant of the conventional perceptron learning algorithm, is presented. The winner-take-all algorithm is proposed as a multilayer training algorithm. A number of examples are presented along with the comparison with backpropagation networks, which demonstrate the performance of Gaussian perceptron networks
  • Keywords
    learning systems; neural nets; parallel algorithms; Gaussian activation function; Gaussian perceptron learning algorithm; Gaussian perceptron networks; learning systems; multilayer training algorithm; neural nets; winner-take-all algorithm; Associative memory; Backpropagation algorithms; Computer networks; Convergence; Employment; Multi-layer neural network; Nearest neighbor searches; Neural networks; Neurons; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on
  • Conference_Location
    Charlottesville, VA
  • Print_ISBN
    0-7803-0233-8
  • Type

    conf

  • DOI
    10.1109/ICSMC.1991.169917
  • Filename
    169917