Support vector machines: A useful tool for process engineering applications

Chemical Engineering Progress

Volumn 99, Issue 1, 2003, Pages 57-62

  Agrawal, M ^a   Jade, A M ^a   Jayaraman, V K ^a   Kulkarni, B D ^a  

^a NATIONAL CHEMICAL LABORATORY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; FUNCTIONS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; PATTERN RECOGNITION; POLYNOMIALS; STATISTICAL METHODS;

LINEAR CLASSIFIER; OPTIMIZATION PROBLEM; PROCESS ENGINEERING PROBLEMS; STATISTICAL LEARNING THEORY; SUPPORT VECTOR MACHINES;

PROCESS ENGINEERING;

EID: 0037279908     PISSN: 03607275     EISSN: None     Source Type: Trade Journal    
DOI: None     Document Type: Article

References (21)

1. Bulsari, A. B., ed., "Neural Networks for Chemical Engineers," Elsevier Science Publishers, Amsterdam (1995).
2. Bulsari, A. B., "Applications of Artificial Neural Networks in Process Engineering," J. Syst. Engng., 4, pp. 131-170 (1994).
3. Hoskins, J. C., and D. M. Himmelblau, "Artificial Neural Network Models of Knowledge Representation in Chemical Engineering," Comput. Chem. Engng., 12, pp. 881 (1988).
4. Wasserman, P. D., "Advanced Methods in Neural Computing," Van Nostrand Reinhold, New York (1993).
5. Vapnik, V., "The Nature of Statistical Learning Theory," Springer, New York (1995).
6. Vapnik, V., "Statistical Learning Theory," Springer, New York (1998).
7. Burges, C. J. C., "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, 2, pp. 121-167 (1998).
8. Christianini, N., and J. Shawe-Taylor, "An Introduction to Support Vector Machines," Cambridge University Press, Cambridge, UK (2000).
9. Drucker, H., et al., "Support Vector Regression Machines,"Neural Information Processing Systems, 9, MIT Press, Cambridge, MA (eds: Mozer, M., Jordan, M., and Petsche, T.) (1997).
10. Smola, A., and B. Schölkopf, "A Tutorial on Support Vector Regression," NeuroCOLT2 Technical Report NC-TR-98-030, Royal Holloway College, Univ. of London, U.K. (1998).
11. Gunn, S., "Support Vector Machines for Classification and Regression," ISIS Technical Report, Dept. of Electronics and Computer Science, Univ. of Southampton (1997).
12. Hoskins, J. C., et al., "Fault Diagnosis in Complex Chemical Plants Using Artificial Neural Networks," AIChE Journal, 37 (1), pp. 137-141 (1991).
13. Chiang, L. H., et al., "Fault Detection and Diagnosis in Industrial Systems," Springer-Verlag, Berlin (2000).
14. Chiang, L. H., et al., "Data-Driven Techniques for Fault Detection and Diagnosis in Chemical Processes," Springer-Verlag, Berlin (2000).
15. Venkatasubramanian, V., et al., "Process Fault Detection and Diagnosis using Neural Networks-1. Steady State Processes," Comp. Chem. Engng., 14 (7), pp. 699-712 (1990).
16. Bhat, N. V., and T. J. McAvoy, "Use of Neural Nets for Dynamic Modeling and Control for Chemical Process Systems," Comp. Chem. Engng., 30, pp. 2563-2573 (1990).
17. Rico-Martinez, R., and I. G. Kevrekidis, "Nonlinear System Identification using Neural Networks using Dynamics and Instabilities," in A. B. Bulsari, Neural Networks for Chemical Engineers, Elsevier, Amsterdam, pp. 409-442 (1995).
18. You, Y., and M. Nikolaou, "Dynamic Process Modeling with Recurrent Neural Networks," AIChE Journal, 39, pp. 1654-1667 (1993).
19. Zhou, X. J., et al., "A Predictive Neural Network Model Based on the Karkhunen-Loéve Expansion for Wall-cooled Fixed-bed Reactors", Canadian Journal Chem. Engng., 74, pp. 638-646 (1996).
20. Luyben, W. L., "Process Modeling, Simulation and Control for Chemical Engineers" (2nd ed.) McGraw-Hill, New York (1990).
21. Weston, J., and C. Watkin, "Support Vector Machines for Multiclass Pattern Recognition," Proceedings of the Seventh European Symposium on Artificial Neural Networks (1999).