Abstract
This paper presents an evolutionary artificial neural network (EANN) to the prediction of the BF hot metal silicon content. The pareto differential evolution (PDE) algorithm is used to optimize the connection weights and the network’s architecture (number of hidden nodes) simultaneously to improve the prediction precision. The application results show that the prediction of hot metal silicon content is successful. Data, used in this paper, were collected from No.1 BF at Laiwu Iron and Steel Group Co..
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© 2005 Springer-Verlag Berlin Heidelberg
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Min, Z., Xiang-guan, L., Shi-hua, L. (2005). An Evolutionary Artificial Neural Networks Approach for BF Hot Metal Silicon Content Prediction. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_46
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DOI: https://doi.org/10.1007/11539087_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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