• DocumentCode
    2040510
  • Title

    Neural control via Hopfield neural network

  • Author

    Lu Jin ; Xu Wenli ; Han Zengjin

  • Author_Institution
    Dept. of Autom., Tsinghua Univ., Beijing, China
  • Volume
    2
  • fYear
    1993
  • fDate
    19-21 Oct. 1993
  • Firstpage
    853
  • Abstract
    Adaptive control of a nonlinear time-variant system has been a dramatic problem attracting many scientists and engineers. As one part of the job of trying to solve the problem, a neural control method based on the Hopfield neural network (HNNMRAC) is presented in this paper. It achieves real-time control of practical plants. Theoretical problems are discussed and simulation results are given to shop the performance of this algorithm.<>
  • Keywords
    Hopfield neural nets; adaptive control; industrial computer control; model reference adaptive control systems; nonlinear control systems; time-varying systems; Hopfield neural network; adaptive control; algorithm performance; model reference adaptive controller; neural control; nonlinear time-variant system; practical plant control; real-time control; simulation results; Biological neural networks; Brain modeling; Delay effects; Equations; Hopfield neural networks; Neural networks; Neurofeedback; Neurons; Operational amplifiers; State feedback;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
  • Conference_Location
    Beijing, China
  • Print_ISBN
    0-7803-1233-3
  • Type

    conf

  • DOI
    10.1109/TENCON.1993.320147
  • Filename
    320147
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2040510