• DocumentCode
    1435524
  • Title

    Artificial neural networks and computational intelligence

  • Author

    King, Roger L.

  • Author_Institution
    Mississippi State Univ., MS, USA
  • Volume
    11
  • Issue
    4
  • fYear
    1998
  • fDate
    10/1/1998 12:00:00 AM
  • Firstpage
    14
  • Abstract
    Artificial neural networks have been applied to power systems in such areas as control, load forecasting, monitoring, diagnosis, and analysis. Their prevalence in the literature and product lines of the electric utility industry has almost overworked the descriptor “intelligent”. However, ANNs do not have an IQ. They exhibit attributes that humans often associate with intelligence: an ability to learn, to communicate, to perceive, or to reason. The objective of this tutorial is to give a basic understanding of ANNs and computational intelligence. This is accomplished by discussing the historical and biological basis of ANNs and reviewing two representative architectures from a simple taxonomy for ANNs. In this tutorial, the use of the term architecture is used to imply both a network topology and a learning rule. Resources for further studies and free software are also recommended
  • Keywords
    learning (artificial intelligence); load forecasting; neural net architecture; neural nets; power system analysis computing; power system control; artificial neural networks; biological basis; computational intelligence; diagnosis; electric utility industry; learning ability; learning rule; load forecasting; monitoring; network topology; neural net architectures; power system analysis; power system control; power systems; self-organisation; supervised learning; unsupervised learning; Artificial neural networks; Computational intelligence; Computer architecture; Control systems; Load forecasting; Monitoring; Power industry; Power system analysis computing; Power system control; Power systems;
  • fLanguage
    English
  • Journal_Title
    Computer Applications in Power, IEEE
  • Publisher
    ieee
  • ISSN
    0895-0156
  • Type

    jour

  • DOI
    10.1109/67.721699
  • Filename
    721699