Abstract
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.
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© 1994 Springer-Verlag London Limited
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Rao, M., Xia, Q., Ying, Y. (1994). Modeling via Artificial Neural Network. In: Modeling and Advanced Control for Process Industries. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-2094-0_9
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DOI: https://doi.org/10.1007/978-1-4471-2094-0_9
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2096-4
Online ISBN: 978-1-4471-2094-0
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