• DocumentCode
    288694
  • Title

    On-line neural network control applications

  • Author

    Colina-Morles, Eliezer

  • Author_Institution
    Fac. de Ingenieria, Los Andes Univ., Merida, Venezuela
  • Volume
    4
  • fYear
    1994
  • fDate
    27 Jun-2 Jul 1994
  • Firstpage
    2494
  • Abstract
    The main objective of this work is to present an exploration of potential applications of online trained neural networks. In particular the work contains computer simulation results obtained from: implementing an internal model neural network-based control scheme, and a model reference neural network-based adaptive control scheme. The design of the online learning algorithm used to adapt the neural networks weights is based on the theory of continuous-time variable structure control systems. Both the selection of the neural network adaptation gain and the parameters of the low-pass filter involved in the scheme have been selected by trial and error. Only stable open-loop systems have been studied
  • Keywords
    adaptive control; feedforward; filtering theory; learning (artificial intelligence); model reference adaptive control systems; neural nets; neurocontrollers; real-time systems; variable structure systems; adaptation gain; continuous-time variable structure control; internal model neural network-based control; low-pass filter; model reference neural network-based adaptive control; online learning; online neural network control; open-loop systems; Application software; Computer simulation; Control systems; Differential equations; Linear systems; Low pass filters; Neural networks; Open loop systems; Output feedback; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1901-X
  • Type

    conf

  • DOI
    10.1109/ICNN.1994.374612
  • Filename
    374612