Response surface methodology: A neural network approach

European Journal of Operational Research

Volumn 101, Issue 1, 1997, Pages 65-73

  Farooq Anjum, M ^a   Tasadduq, Imran ^b   Al Sultan, Khaled ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; FUNCTION EVALUATION; ITERATIVE METHODS; OPTIMIZATION; PARAMETER ESTIMATION;

RESPONSE FUNCTIONS;

NEURAL NETWORKS;

EID: 0031199110     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(96)00232-9     Document Type: Article

References (25)

1. Shang, J.S., and Tadikamalla, P.R., "Output maximization of a cim system: simulation and statistical approach", International Journal of Production Research, 31 (1993) 19-41.
2. Montgomery, D.C., and Bettencourt Jr., V.M., "Multiple response surface methods in computer simulation", Simulation (1977) 113-121.
3. Barnwal, S., Bayoumi, A.E., and Hutton, D.V., "Prediction of flank wear and engagement from force measurements in end milling operations", Wear 170 (1993) 255-266.
4. Christopher, M., Su, H., and Ismail, M., "Yield optimization of analog mos integrated circuits including transistor mismatch", Proceedings of 1993 IEEE International Symposium on Circuits and Systems 3 (1993) 1801-1804.
5. Fujiwara, O., Dharmappa, H.B., Vernik, J., and Vigneswaran, S., "Optimization of granular bed filtration treating polydispersed suspension", Water Resources 26 (1992) 1307-1318.
6. Chen, S.H., Grobelny, P.A., Bandler, J.W., Biernacki, R.M., and Ye, S., "Yield-driven electromagnetic optimization via multilevel multidimensional models", IEEE Transactions on Microwave Theory and Techniques 41 (1993) 2269-2278.
7. Hewidy, M.S., and Fattouh, M., "Electrochemical cutting using tubular cathodes: response surface approach", International Journal of Production Research 27 (1989) 953-963.
8. Geoffrey, G.V., and Myers, R.H., "Combining Taguchi and response surface philosophies: A dual response approach", Journal of Quality Technology 22 (1990) 38-45.
9. Hornik, H., Stinchcomb, M., and White, J., "Multilayer feedforward networks are universal approximators", Neural Networks 2 (1982) 359-366.
10. Cybenko, G., "Approximation by superposition of a sigmoidal function", Math Control Signal Systems 2 (1989) 303-314.
11. Hinton, G.E., "How neural networks learn from experience", Scientific American, September (1992) 105-109.
12. Hunt, K.J., and Sbarbaro, D., "Neural networks for nonlinear internal model control", IEE Proceedings-D 138 (1993) 431-438.
13. Sotirov, G., and Shafai, B., "Interval identification of nonlinear systems using neural networks", Proceedings of the American Control Conference, San Francisco, CA, (1993) 2529-2530.
14. Montgomery, D.C., and Rungers, G.C., Applied Statistics and Probability for Engineers, John Wiley, New York, 1994.
15. Tew, J.D., "Simulation metamodel estimation using a combined correlation-based variance reduction technique for first and higher-order metamodels", European Journal of Operational Research 87 (1993) 349-363.
16. Kleijnen, J., and Groenendaal, W.V., Simulation: A Statistical Perspective, John Wiley, Londen, 1992.
17. Lin, D.K., and Tu, W., "Dual response surface optimization", Journal of quality technology 27 (1995) 34-39.
18. Myers, R.H., and Khuri, A.I., "Response surface methodology: 1966-1988", Technometrics 31 (1989) 137-157.
19. Myers, R.H., Vining, G.G., Goivannitti-Jensen, A., and Myers, S.L., "Variance dispersion properties of second-order response surface designs", Journal of Quality Technology 24 (1992) 1-11.
20. Vining, G.G., and Myers, R.H., "Combining Taguchi and response surface philosophies: A dual response approach", Journal of Quality Technology 22 (1990) 38-45.
21. Castillo, E.D., and Montgomery, D.C., "A nonlinear programming solution to the dual response problem", Journal of Quality Technology 25 (1993) 199-204.
22. Ahmed, M.S., and Tasadduq, I.A., "Neural-net controller for nonlinear plants: design approach through linearization", IEE Proc. Control Theory Appl. 141 (1994) 323-328.
23. Webros, P.J., "Backpropagation through time: what it does and how to do it", Proc. of IEEE 78, 1550-1560,
24. Rumelhart, D.E., Hinton, G.E., and Williams, R.J., "Learning internal representation by back propagation", Parallel Distributed Processing: Exploration in the Microstructure of Cognition, 1, 1986.
25. Bazaraa, M.S., Sherali, H.D., and Shetty, C.M., Nonlinear Programming: Theory and Algorithms, 2nd Ed., John Wiley, New York, 1993.