Abstract
Dynamic modeling for the quality of large-scale process is studied in this paper combined with continuous annealing process. This kind of process is constituted with several sub-processes. There is complex nonlinear mapping between the sub-process set points and the final quality. The quality model should be constructed and updated based on the new data from the real process. To meet this demand, a novel generalized growing and pruning RBF (GGAP-RBF) network is used to establish the quality model. GGAP-RBF is a sequential learning algorithm so that we can establish the model dynamically. Last, we do some on-line application study on the continuous annealing furnace in a steel factory. The quality model between the furnace temperature of each zone in the furnace and the exit strip temperature is constructed.
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© 2004 Springer-Verlag Berlin Heidelberg
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Chen, Q., Li, S., Xi, Y., Huang, G. (2004). Furnace Temperature Modeling for Continuous Annealing Process Based on Generalized Growing and Pruning RBF Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_121
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DOI: https://doi.org/10.1007/978-3-540-28648-6_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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