Stability and Performance of Neural Regulators

  • Sørensen, Paul Haase (Project Manager)
  • Ravn, Ole (Project Participant)
  • Luther, Jim Benjamin (Project Participant)

    Project Details


    During the last 10 years there has been a substancial development in the technical applications of neural networks in different applications, such as image processing, signal processing and control. In control the neural networks enter in two inherently different ways; either directly in the control loop or indírectly via a non-linear neural netowkr model of the plant. The direct method often has poor stability and performance robustness, whereas it should be possible to make qualified statements about the stability and performance of the indirect neutral control system.
    Ith is the aim of this project to investigate the stability and performance robustness of indirect neural network controllers in particular the promising type of neural network predictive controllers.
    Effective start/end date01/09/199831/08/2001


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