• DocumentCode
    1905191
  • Title

    A fault tolerant optimal interpolative net

  • Author

    Simon, Dan ; El-Sherief, Hossny

  • Author_Institution
    TRW Syst. Integration Group, San Bernardino, CA, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    825
  • Abstract
    The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI Net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault tolerant OI Net is presented in a recursive format, allowing for relatively short training times. A simulated fault tolerant OI Net is tested on a navigation satellite selective problem
  • Keywords
    interpolation; learning (artificial intelligence); neural nets; pattern recognition; reliability; classification network; fault tolerant optimal interpolative net; recursive learning algorithm; Biological systems; Fault tolerance; Fault tolerant systems; Neural networks; Neurons; Prototypes; Robustness; Satellite navigation systems; Testing; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298665
  • Filename
    298665