Abstract
In this paper, a modified version of the Support Vector Machine (SVM) is proposed as an empirical model for polymerization processes modeling. Usually the exact principle models of polymerization processes are seldom known; therefore, the relations between input and output variables have to be estimated by using an empirical inference model. They can be used in process monitoring, optimization and quality control. The Support Vector Machine is a good tool for modeling polymerization process because it can handle highly nonlinear systems successfully. The proposed method is derived by modifying the risk function of the standard Support Vector Machine by using the concept of Locally Weighted Regression. Based on the smoothness concept, it can handle the correlations among many process variables and nonlinearities more effectively. Case studies show that the proposed method exhibits superior performance as compared with the standard SVR, which is itself superior to the traditional statistical learning machine in the case of high dimensional, sparse and nonlinear data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cherkassky, W. and Mulier, F., “Learning from Data,” John Wiley & Sons, US (1998).
Cleveland, R. J. and McArthur, J. M., “Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting,”Journal of American Statistical Association,83, 596 (1988).
Cristianini, N. and Shawe-Taylor, J., “An Introduction to Support Vector Machines,” Cambridge Univ. Press, UK (2001).
DeVeaux, R. D., Psichogios, D. C. and Ungar, L. H., “A Comparison of Two Nonparametric Estimation Schemes: MARS and Neural Networks,”Com. & Chem. Eng.,17(8), 819 (1993).
Gunn, S. R., Brown, M. and Bossley, K. M., “Network Performance Assessment for Neurofuzzy Data Modeling,”Intelligent Data Analysis,1208, 313 (1997).
Kecman, N., “Learning and Soft Computing,” MIT Press, UK (2001).
Kim, H. J. and Chang, K S., “Hybrid Neural Network Approach in Description and Prediction of Dynamic Behaviour of Chaotic Chemical Reaction Systems,”Korean J. Chem. Eng.,17, 696 (2000).
Kresta, J. V., Marlin, T. E. and MacGregor, J. F., “Development of Inferential Process Models using PLS,”Com. & Chem. Eng.,18(7), 597 (1994).
Liu, J., Min, K., Han, C. and Chang, K. S., “Robust Nonlinear PLS Based on Neural Networks and Application to Composition Estimator for High-purity Distillation Columns,”Korean J. Chem. Eng.,17, 184 (2000).
Psichogios, D. C., DeVeaux, R D. and Ungar, L. H., “Nonparametric System Identification: A Comparison of MARS and Neural Networks,” Proceedings of the ACC, 1436 (1992).
Seymour, S. B. and Carraher, C. E., “Polymer Chemistry an Introduction,” Marcel Dekker, New York (1988).
Skagerberg, B., MacGregor, J. F. and Kiparissides, C., “Multivariate Data Analysis Applied to Low-density Polyethylene Reactors,”Chemometrics of Intelligent Laboratory Systems,14, 341 (1992).
Vapnik, V., “The Nature of Statistical Learning Theory,” Springer, US (1998).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, D.E., Song, SO. & Yoon, E.S. Weighted support vector machine for quality estimation in polymerization processes. Korean J. Chem. Eng. 21, 1103–1107 (2004). https://doi.org/10.1007/BF02719481
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02719481