Toward automated intelligent manufacturing systems (AIMS)

INFORMS Journal on Computing

Volumn 19, Issue 2, 2007, Pages 302-312

  Chi, Hoi Ming ^a   Ersoy, Okan K ^a   Moskowitz, Herbert ^b   Altinkemer, Kemal ^b  

^a PURDUE UNIVERSITY   (United States)

^b PURDUE UNIVERSITY   (United States)

Author keywords

Computational intelligence; Design of experiments (DOE); Genetic algorithm; Intelligent systems; Machine learning; Response surface designs; Six sigma; Support vector machine

Indexed keywords

EID: 61349163620     PISSN: 10919856     EISSN: 15265528     Source Type: Journal    
DOI: 10.1287/ijoc.1050.0171     Document Type: Article

References (30)

1. Baesens, B., R. Setiono, C. Mucs, J. Vanthienen. 2003. Using neural network rule extraction and decision tables for credit-risk evaluation. Management Sci. 49 312-329.
2. Buetow, R. 1996. The Motorola quality process: Six Sigma. G. M. Bounds, ed. Cases in Quality. Irwin, Chicago, IL, 229-249.
3. Chipperfield, A. J., P. J. Fleming, H. Pohlheim, C. M. Fonseca. 1994. Genetic algorithm toolbox user's guide. ACSE Research Report 512, University of Sheffield, Sheffield, UK.
4. Chong, E. K. P., S. H. Zak. 1996. An Introduction to Optimization. Wiley, New York.
5. Cukicr, R. I., C. M. Fortuin, K. E. Shuler, A. G. Petschek, J. H. Schaibly. 1973. Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory. J. Chemical Phys. 59 3873-3878.
6. Draper, N. R., H. Smith. 1981. Applied Regression Analysis, 2nd ed. Wiley, New York.
7. Fadlalla, A., C. H. Lin. 2001. An analysis of the applications of neural networks in finance. Interfaces 31 112-122.
8. Fassò, A. 2002. Nonlinear multivariate statistical sensitivity analysis of environmental models with application to heavy metal adsorption from contaminated wastewater. Internat. Environ-metrics Soc. (TIES) Conf, Genova, Italy.
9. Fletcher, R. 1987. Practical Methods of Optimization. Wiley, New York.
10. Harry, M. J. 1994a. The Vision of Six Sigma: A Roadmap for Breakthrough. Sigma, Phoenix, AZ.
11. Harry, M. J. 1994b. The Vision of Six Sigma: Tools and Methods for Breakthrough. Sigma, Phoenix, AZ.
12. Hastie, T, R. Tibshirani, J. Friedman. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York.
13. Joachims, T. 1998. Making large-scale SVM learning practical. B. Scholkopf, C. Surges, A. Smola, eds. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, MA, 169-184.
14. Muller, K. R., A. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, V. Vapnik. 1997. Predicting time series with support vector machines. Internat. Conf. Artificial Neural Networks (ICANN), Lausanne, Switzerland, 999-1004.
15. Ortiz, F, J. R. Simpson, J. J. Pignatiello, A. Heredia-Langner. 2004. A genetic algorithm approach to multiple-response optimization. J. Quality Tech. 36 432-450.
16. Saltelli, A., K. Chan, E. M. Scott. 2000. Sensitivity Analysis. Wiley, Chichester, England.
17. Schwaighofer, A. 2002. MATLAB Interface to SVM Light. Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria, http://www.cis.tugraz.at/igi/aschwaig/software.html.
18. Seo, H., H. Moskowitz. 2002. Six Sigma Simulator. Unpublished Research Report, Krannert School of Management, Purdue University, West Lafayette, IN.
19. Smola, A., B. Scholkopf. 1998. A tutorial on support vector regression. NeuroCOLT2 Technical Report Series, NC2-TR-1998-030.
20. Sobol, I. M. 1993. Sensitivity analysis for nonlinear mathematical models. Math. Model. Comput. Exp. 1 407-414.
21. Suykens, J. A. K., J, Vandewalle. 1999. Least squares support vector machine classifiers. Neural Processing Lett. 9 293-300.
22. Suykens, J. A. K., J. Vandewalle. 2000. Recurrent least squares support vector machines. IEEE Trans. Circuits and Systems-I 47 1109-1114.
23. Suykens, J. A. K., J. De Brabanter, L. Lukas, J. Vandewalle. 2002a. Weighted least squares support vector machines: Robustness and sparse approximation. Neurocomputing 48 85-105.
24. Suykens, J. A. K., T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle. 2002b. Least Squares Support Vector Machines. World Scientific, Singapore, http://www.esat.kuleuven.ac.be/sista/lssvmlab/.
25. Suykens, J. A. K., T. Van Gestel, J. Vandewalle, B. De Moor. 2002c. A support vector machine formulation to PCA analysis and its kernel version. Internal Report 02-68, ESAT-SISTA, K. U. Leuven, Leuven, Belgium.
26. Trafalis, T. B., H. Ince. 2000. Support vector machine for regression and applications to financial forecasting. Internat. Joint Conf. Neural Network (IJCNN) 2000, Como, Italy, 348-353.
27. Van Gestel, T, J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedenc, B. De Moor, J. Vandewalle. 2004. Benchmarking least squares support vector machine classifiers. Machine Learn. 54 5-32.
28. Vapnik, V. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
29. Vapnik, V., S. Golowich, A. Smola. 1997. Support vector method for function approximation, regression estimation and signal processing. M. Mozer, M. Jordan, T. Petsche, eds. Advances in Neural Information Processing Systems, Vol. 9. MIT Press, Cambridge, MA.
30. Wang, S., W. Zhu, Z. Liang. 2001. Shape deformation: SVM regression and application to medical image segmentation. Internat. Conf. Comput. Vision (ICCV'O1), Vol. 2. Vancouver, BC, Canada, 209-216.