Abstract
An inferential estimation strategy of quality indexes of flotation process based on principal component analysis (PCA) and radial basis function neural network (RBFNN) is proposed. Firstly, the process prior knowledge and PCA method are used to simplify the networks’ input dimension and to choose the secondary variables. Then a new hybrid optimization algorithm of RBFNN is developed. The algorithm includes simplified rival penalized competitive learning method (SRPCL) to make an adaptive clustering of networks’ input pattern and recursive least squares method (LSM) with forgetting factor to update networks’ weights. The simulation results show that this inference estimation strategy has high predictive accuracy in flotation process.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Wang, JS. (2006). Application of RBF Neural Networks Based on a New Hybrid Optimization Algorithm in Flotation Process. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_157
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DOI: https://doi.org/10.1007/11760191_157
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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