• DocumentCode
    1800014
  • Title

    An adequate training set for the AIMNC strategy for typical industrial processes

  • Author

    Igic, Jasmin ; Bozic, Milorad

  • Author_Institution
    mtel a.d. Banja Luka, Banja Luka, Bosnia-Herzegovina
  • fYear
    2014
  • fDate
    25-27 Nov. 2014
  • Firstpage
    183
  • Lastpage
    188
  • Abstract
    Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.
  • Keywords
    neurocontrollers; process control; AIMNC strategy; approximate internal model; industrial process; inverse neural controller; multiLayer NN; neural network; training set; Approximation methods; Artificial neural networks; Autoregressive processes; Process control; Steady-state; Training; Vectors; Industrial processes; neural networks; nonlinear internal model control; training set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
  • Conference_Location
    Belgrade
  • Print_ISBN
    978-1-4799-5887-0
  • Type

    conf

  • DOI
    10.1109/NEUREL.2014.7011502
  • Filename
    7011502