Modeling via Artificial Neural Network

  • Ming Rao
  • Qijun Xia
  • Yiqun Ying
Part of the Advances in Industrial Control book series (AIC)


In this chapter, artificial neural network technology is applied to predict the basis weight and moisture content to improve the paper product quality. Historical data from a paper production company in Canada are analyzed and applied to train a multilayer feedforward backpropagation network. Considering that generalized descent method, which is a typical optimization algorithm in backpropagation, has some major drawbacks, a conjugated gradient method is proposed for training neural networks. The results have shown that the neural network gives accurate paper quality prediction. The application of artificial neural network helps us to gain a better understanding of dependence of quality variables on the operating conditions and to overcome large time-delay in paper machine control systems.


Neural Network Artificial Neural Network Neural Network Model Basis Weight Steam Pressure 
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 1994

Authors and Affiliations

  • Ming Rao
    • 1
  • Qijun Xia
    • 1
  • Yiqun Ying
    • 1
  1. 1.Department of Chemical EngineeringUniversity of AlbertaEdmontonCanada

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