Abstract
Aim at the problems occurring in a least square method model and a neural network model for flatness pattern recognition, a new approach of flatness pattern recognition based on the wavelet transform(WT) and probabilistic neural network(PNN) is proposed to meet the demand of high-precision flatness control for cold strip mill. Energy distribution at different scales of stress distribution within strip steel is derived by WT with which severed as feature vectors. Then the feature vectors act as input vectors of PNN for target classification. The energy vectors by WT can differentiate various stress distribution. The design of PNN is straightforward and does not depend on training. PNN is suitable for signal classification. The model is shown to fit the actual data precisely. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously improved.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, J., Zhang, R., Song, W. (2012). A Flatness Pattern Recognition Model Based on Wavelet Transform and Probabilistic Neural Network. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_28
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DOI: https://doi.org/10.1007/978-3-642-34041-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34040-6
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