• DocumentCode
    2751050
  • Title

    Design of ANN (artificial neural networks)-fast backpropagation algorithm gain scheduling controller of active filtering

  • Author

    Gulez, Kayhan ; Watanabe, Hiroshi ; Harashima, Fumio

  • Author_Institution
    Dept. of Electron. Syst. Eng., Tokyo Metropolitan Inst. of Technol., Japan
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    18
  • Abstract
    The application of ANN (artificial neural networks) to active circuitry to increase the performance per size, prevent dependency on some parameters of electromagnetic interference (EMI) filter and determine the circuit gain directly are considered. The major problems are power line frequency rejection and the compensation of the feedback loop, which is influenced by the wide-ranging utility impedance. While analysis and simulations show, in the literature, that these problems prevent the practical application of active filtering to power supplies especially at less than 100 kHz, the approximation easily demonstrates a good promise to ensure the design of the architecture of a gain scheduling controller by using ANN for active filtering
  • Keywords
    active filters; backpropagation; circuit CAD; electromagnetic interference; gain control; network synthesis; neural nets; EMI filter; active circuitry; active filtering; artificial neural networks; circuit gain; electromagnetic interference; fast backpropagation algorithm; feedback loop compensation; gain scheduling controller; power line frequency rejection; power supplies; simulations; utility impedance; Active filters; Analytical models; Artificial neural networks; Backpropagation; Circuits; Electromagnetic interference; Feedback loop; Frequency; Impedance; Performance gain;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON 2000. Proceedings
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    0-7803-6355-8
  • Type

    conf

  • DOI
    10.1109/TENCON.2000.893532
  • Filename
    893532