1999, English, Article edition: A Neural Network with Minimal Structure for Maglev System Modeling and Control Lairi, Mostafa; Bloch, Gérard

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Bookmark: http://trove.nla.gov.au/version/208829
Physical Description
  • text
Published
  • HAL - CCSD
  • 1999
Language
  • English

Edition details

Title
  • A Neural Network with Minimal Structure for Maglev System Modeling and Control
Author
  • Lairi, Mostafa
  • Bloch, Gérard
Published
  • HAL - CCSD
  • 1999
Physical Description
  • text
Subjects
Notes
  • The paper is concerned with the determination of a minimal structure of a one hidden layer perceptron for system identification and control. Structural identification is a key issue in neural modeling. Decreasing the size of the neural networks is a way to avoid overfitting and bad generalization and leads moreover to simpler models which are required for real time applications, particularly in control. A learning algorithm and a pruning method both based on a criterion robust to outliers are presented. Their performances are illustrated on a real example, the inverse model identification of a MagLev system, which is nonlinear, dynamical and rapid. This inverse model is used in a feedforward neural control scheme. Very satisfactory approximation performances are obtained for a network with very few parameters.
  • HAL:hal-00405124, version 1
  • HAL:http:/​/​hal.archives-ouvertes.fr/​hal-00405124/​en/​
  • DOI:10.1109/​ISIC.1999.796627
Language
  • English
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