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Received January 28, 2004
Accepted September 24, 2004
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Weighted Support Vector Machine for Quality Estimation in Polymerization Processes
School of Chemical Engineering, Seoul National University, Seoul 151-742, Korea
Korean Journal of Chemical Engineering, November 2004, 21(6), 1103-1107(5), 10.1007/BF02719481
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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._x000D_
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References
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Cleveland RJ, McArthur JM, J. Am. Stat. Assoc., 83, 596 (1988)
Cristianini N, Shawe-Taylor J, "An Introduction to Support Vector Machines," Cambridge Univ. Press, UK (2001)
DeVeaux RD, Psichogios DC, Ungar LH, Comput. Chem. Eng., 17(8), 819 (1993)
Gunn SR, Brown M, Bossley KM, Intelligent Data Analysis, 1208, 313 (1997)
Kecman N, "Learning and Soft Computing," MIT Press, UK (2001)
Kim HJ, Chang KS, Korean J. Chem. Eng., 17(6), 696 (2000)
Kresta JV, Marlin TE, Macgregor JF, Comput. Chem. Eng., 18(7), 597 (1994)
Liu J, Min K, Han C, Chang KS, Korean J. Chem. Eng., 17(2), 184 (2000)
Psichogios DC, DeVeaux RD, Ungar LH, "Nonparametric System Identification: A Comparison of MARS and Neural Networks," Proceedings of the ACC, 1436 (1992)
Seymour SB, Carraher CE, "Polymer Chemistry an Introduction," Marcel Dekker, New York (1988)
Skagerberg B, MacGregor JF, Kiparissides C, Chemometrics Intell. Lab. Syst., 14, 341 (1992)
Vapnik V, "The Nature of Statistical Learning Theory," Springer, US (1998)
