• DocumentCode
    2409082
  • Title

    Weight value testing in artificial neural networks

  • Author

    Belfore, Lee A., II

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA
  • Volume
    5
  • fYear
    1997
  • fDate
    12-15 Oct 1997
  • Firstpage
    4023
  • Abstract
    This paper presents a method for testing ANN interconnection weight values in programmable ANNs. The methodology requires separate weights for excitatory and inhibitory contributions to a neuron´s activation. In the test the excitatory and inhibitory weights are set to be equal, producing no net contribution to the activation. Any faults in the weight values result in an imbalance that is amplified by the neuron activation function and is easily detectable at the neuron output. Analysis is performed on the detection approach that includes fault detection thresholds as a function of neuron gain, network size, and weight perturbation. A methodology is outlined that uses the proposed testing approach followed by example test results
  • Keywords
    neural net architecture; neural nets; activation; artificial neural networks; excitatory; fault detection thresholds; inhibitory; interconnection weight values; neuron activation function; weight value testing; Artificial neural networks; Circuit faults; Circuit testing; Fault detection; Integrated circuit interconnections; Intelligent networks; Manufacturing; Neurons; Performance analysis; Performance gain;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-4053-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.1997.637287
  • Filename
    637287