Modeling delayed coking process by adaptive support vector machine

Huagong Xuebao/Journal of Chemical Industry and Engineering (China)

Volumn 55, Issue 1, 2004, Pages 147-150

  Song, Xiaofeng ^a   Yu, Huanjun ^a   Chen, Dezhao ^a   Hu, Shangxu ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Delayed coking; Modeling; Support vector machine; Tuning parameter

Indexed keywords

ADAPTIVE ALGORITHMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROCESS ENGINEERING; REGRESSION ANALYSIS;

DELAYED COKING PROCESS; SUPPORT VECTOR MACHINE; SUPPORT VECTOR REGRESSION ESTIMATION; TUNING PARAMETER;

COKING;

EID: 2342439801     PISSN: 04381157     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (7)

1. Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1995
2. Zhang Xuegong. Introduction to Statistical Learning Theory and Support Vector Machine. Acta Automatica Sinica, 2000, 26(1): 32-42
3. Lin C J. On the Convergence of the Decomposition Method for Support Vector Machines. IEEE Transactions on Neural Networks, 2001, 12(6): 1288-1298
4. Liao S P, Lin H T, Lin C J. A Note on the Decomposition Methods for Support Vector Regression. Neural Computation, 2002 (14), 1267-1281
5. Zhao Weixiang. Novel Methods for Establishing the Reaction Model of Complex Material System Based on Observation Data: [dissertation]. Hangzhou: Zhejiang University, 2000
6. Walczak B, Massart D L. The Radial Basis Function-Partial Least Squares Approach as a Flexible Non-linear Regression Technique. Analytica Chimica Acta, 1996, 331: 177-185
7. Li Zhihua, Chen Dezhao, Zhuang Ling, Hu Shangxu. RBF-MCSR Approach as Modeling Technique for Equipment of Isomerization of Xylene. Journal of Chemical Industry and Engineering (China), 2002, 53(6): 627-632