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A Radial Basis Function Network Model for the Adaptive Control of Drying Oven Temperature

  • Olivier Dubois
  • Jean-Louis Nicolas
  • Alain Billat
Part of the Advances in Industrial Control book series (AIC)

Abstract

Artificial neural networks are new modelling tools for process control, especially in non-linear dynamic systems. They have been shown to successfully approximate non-linear relationships. This paper describes a neural control scheme for the temperature in a drying oven. The control strategy used is internal model control, in which the plant is modelled by a radial basis function network. The process was identified in an off-line phase —training the network while determining its optimal structure— and an on-line phase —adapting the neural model to any change in the process dynamics. The control strategy was tested experimentally in a series of trials with the oven empty and loaded.

Keywords

Hide Layer Model Predictive Control Nonlinear Dynamic System Radial Basis Function Neural Network Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1995

Authors and Affiliations

  • Olivier Dubois
    • 1
  • Jean-Louis Nicolas
    • 1
  • Alain Billat
    • 1
  1. 1.Laboratoire d’Applications de la MicroélectroniqueUniversité de Reims Champagne ArdenneReims CedexFrance

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