• DocumentCode
    976947
  • Title

    Modified LMS adaptive algorithm for CMAC neural network based control of switched reluctance motors

  • Author

    Shang, C. ; Reay, D.S. ; Williams, B.W.

  • Author_Institution
    Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK
  • Volume
    32
  • Issue
    12
  • fYear
    1996
  • fDate
    6/6/1996 12:00:00 AM
  • Firstpage
    1113
  • Lastpage
    1115
  • Abstract
    A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency
  • Keywords
    cerebellar model arithmetic computers; least mean squares methods; machine control; neurocontrollers; reluctance motors; CMAC neural network; LMS adaptive algorithm; control; learning rate function; power efficiency; simulation; switched reluctance motor; torque ripple; training;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:19960721
  • Filename
    502886
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=976947