• DocumentCode
    835509
  • Title

    Introduction to neural networks for intelligent control

  • Author

    Bavarian, Behnam

  • Author_Institution
    Robotics Res. Lab., California Univ., Irvine, CA, USA
  • Volume
    8
  • Issue
    2
  • fYear
    1988
  • fDate
    4/1/1988 12:00:00 AM
  • Firstpage
    3
  • Lastpage
    7
  • Abstract
    Neural network architecture is presented as one approach to the design and implementation of intelligent control systems. Neural networks can be considered as massively parallel distributed processing systems with the potential for ever-improving performance through dynamical learning. The nomenclature and characteristics of neural networks are outlined. Two simple examples are presented to illustrate applications to control systems: one is fault isolation mapping, and the other involves optimization of a Hopfield network that defines a clockless analog-to-digital conversion.<>
  • Keywords
    computer architecture; control system synthesis; learning systems; neural nets; parallel processing; Hopfield network; clockless analog-to-digital conversion; control design; dynamical learning; fault isolation mapping; intelligent control; intelligent control systems; massively parallel distributed processing systems; neural network architecture; optimization; Adaptive control; Control systems; Distributed processing; Feedback control; Feedback loop; Intelligent control; Neural networks; Robust control; Sensor systems; Uncertainty;
  • fLanguage
    English
  • Journal_Title
    Control Systems Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1708
  • Type

    jour

  • DOI
    10.1109/37.1866
  • Filename
    1866