• DocumentCode
    2680021
  • Title

    Neural network and its ancillary techniques as applied to power systems

  • Author

    El-Sharkawi, M.A.

  • Author_Institution
    Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
  • fYear
    1995
  • fDate
    34809
  • Firstpage
    42430
  • Lastpage
    42435
  • Abstract
    The layered perceptron neural net is receiving the most attention as a viable candidate for application to power systems. The layered perceptron is taught by example. Before neural networks can gain the necessary recognition as useful problem solving tools in the power industry, certain fundamental issues need to be addressed. Some of them are associated with neural network technology, and others are problem dependent. The author discusses the following issues: learning versus memorisation; best net size determination, network saturation, feature extraction, neural net inversion, genetic based neural nets, and fuzzified neural nets
  • Keywords
    learning (artificial intelligence); multilayer perceptrons; power system analysis computing; best net size determination; feature extraction; fuzzified neural nets; genetic based neural nets; layered perceptron neural net; learning; network saturation; neural net inversion; power systems; problem solving tools;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Artificial Intelligence Applications in Power Systems, IEE Colloquium on
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1049/ic:19950495
  • Filename
    477911