Melt Index Predict by Radial Basis Function Network Based on Principal Component Analysis
Melt index is considered important quality variable determining product specifications. Reliable prediction of melt index (MI) is crucial in quality control of practical propylene polymerization processes. In this paper, a radial basis function network (RBF) model based on principal component analysis (PCA) and genetic algorithm (GA) is developed to infer the MI of polypropylene from other process variables. Considering that the genetic algorithm need long time to converge, chaotic series are explored to get more effective computation rate. The PCA-RBF model is also developed as a basis of comparison research. Brief outlines of the modeling procedure are presented, followed by the procedures for training and validating the model. The research results confirm the effectiveness of the presented methods.
KeywordsRoot Mean Square Error Radial Basis Function Receptive Field Radial Basis Function Network Back Propagation Neural Network
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- 3.Kong, W., Yang, J.: Prediction of polypropylene melt index based on RBF networks. J. Chem. Ind. Eng (Chinese) 54(8), 1160–1163 (2003)Google Scholar
- 4.Shi, J., Liu, X.: Melt index prediction by neural soft-sensor based on multi-scale analysis and principal component analysis. Chinese J. Chem. Eng. 13(6), 849–852 (2005)Google Scholar
- 5.Moody, J., Darken, C.: Learning with localized receptive fields. In: Touretzky, D., Hinton, G., Sejnowski, T. (eds.) Proc. 1988 Connectionist Models Summer School, Carnegie Mellon University. Morgan Kaufmann Publishers, San Francisco (1988)Google Scholar
- 7.Zheng, C., Jiao, L.: Automatic parameters selection for SVM based on GA. In: Proceedings of the 5th world congress on intelligent control and automation, Hangzhou, P.R. China (2004)Google Scholar