Developing optimal neural network metamodels based on prediction intervals

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2009, Pages 1583-1589

  Khosravi, Abbas ^a   Nahavandi, Saeid ^a   Creighton, Doug ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX SYSTEMS; INTERVAL PREDICTION; MEAN ABSOLUTE PERCENTAGE ERROR; META MODEL; NETWORK STRUCTURES; OPEN PROBLEMS; OPTIMAL NEURAL NETWORK; OPTIMAL STRUCTURES; PREDICTION INTERVAL; PREDICTION PERFORMANCE; RELIABLE ESTIMATES; SCIENCE AND ENGINEERING; STOCHASTIC NATURE; STOCHASTIC PROCESS;

LARGE SCALE SYSTEMS; RANDOM PROCESSES; STOCHASTIC SYSTEMS;

NEURAL NETWORKS;

EID: 70449371520     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2009.5178591     Document Type: Conference Paper

References (26)

1. J. J. P. C. Kleijnen, Kriging Metamodeling In Simulation: A Review, European Journal of Operational Research, Vol. 192, 2009, pp. 707- 716.
2. S. M. Clarke, J. H. Griebsch, T. W. Simpson, "Analysis of support vector regression for approximation of complex engineering analyses", ASME Journal of Mechanical. Design, Vol. 127, pp. 1077- 1087.
3. L. Yu, S. Wang, K. K. Lai, "A neural-network-based nonlinear metamodeling approach to financial time series forecasting", Applied Soft Computing, In Press, 2008.
4. Y. Kuo, T. Yang, B. A. Peters, I. Chang, "Simulation Metamodel Development Using Uniform Design And Neural Networks For Automated Material Handling Systems In Semiconductor Wafer Fabrication", Simulation Modelling Practice and Theory, Vol. 15, 2007, pp. 1002-1015.
5. F. M. Alam, K. R. McNaught, T. J. Ringrose, "A comparison of experimental designs in the development of a neural network simulation metamodel", Simulation Modelling Practice and Theory, Vol. 12, 2004, pp 559-578.
6. T. Chen, "A Fuzzy Back Propagation Network For Output Time Prediction In A Wafer Fab", Journal of Applied Soft Computing 2/3F, 2003, pp. 211-222.
7. E. E. Tabach, L. Lancelot, I. Shahrour, Y. Najjar, "Use of artificial neural network simulation metamodelling to assess groundwater contamination in a road project", Mathematical and Computer Modelling, Vol. 45, 2007, pp. 766-776.
8. M. Chambers, C. A. Mount-Campbell, "Process optimization via neural network metamodeling", International Journal of Production Economics, Vol. 79, 2002, pp. 93-100.
9. F. Altiparmak, B. Dengiz, A. A. Bulgak, "Buffer Allocation And Performance Modeling In Asynchronous Assembly System Operations: An Artificial Neural Network Metamodeling Approach", Applied Soft Computing, Vol. 7, No. 3, 2007, pp. 946-956.
10. R. D. Hurrion, An Example of Simulation Optimisation Using a Neural Network Metamodel: Finding the Optimum Number of Kanbans in a Manufacturing System, Journal of the Operational Research Society, Vol. 48, No. 11, 1997, pp. 1105-1112.
11. D. J. Fonseca, D. O. Navaresse, G. P. Moynihan, "Simulation Metamodeling Through Artificial Neural Networks", Engineering Applications of Artificial Intelligence, Vol. 16, 2003, pp. 177-183.
12. M. T. Hagan, H. B. Demuth, M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.
13. E. Baum, B. D. Haussler, "What size net gives valid generalization?", Neural Computation, Vol. 1, 1989, pp. 151-160.
14. Z. Yan, L. Yingrong, L. Qian, "MW-OBS: An Improved Pruning Method for Topology Design of Neural Networks", Tsinghua Science and Technology, Vol. 11, No. 3, 2006, pp. 307-312.
15. M. Attik., L. Bougrainl, F. Alexandre, "Neural Network Topology Optimization", ICANN 2005, LNCS 3697, 2005, pp. 53-58.
16. J. Branke, "Evolutionary Algorithms for Neural Network Design and Training", Proceeding of the first Nordic Workshop on Genetic Algorithm and its Application, Finland, 1995.
17. R. A. Kilmer, A. E. Smith, L. J. Shuman, "Computing confidence intervals for stochastic simulation using neural network metamodels", Computers & Industrial Engineering, Vol. 36, 1999, pp. 391-407.
18. C. W. Zobel, K. B. Keeling, "Neural network-based simulation metamodels for predicting probability distributions", Computers & Industrial Engineering, Vol. 54, 2008, pp. 879-888.
19. G. Chryssolouris, L. Moshin, A. Ramsey, "Confidence interval prediction for neural network models", IEEE Transactions on Neural Networks, Vol. 7, No. 1, 1996, pp. 229-232.
20. G. Papadopoulos, P. J. Edwards, A. F. Murray, "Confidence Estimation Methods for Neural Networks: A Practical Comparison", IEEE Transactions on Neural Networks, Vol. 12, No. 6, 2001, pp. 1278-1287.
21. T. Heskes, "Practical confidence and prediction interval", Advances in Neural Information Processing Systems 9, Cambridge, 1997, MIT Press, pp. 176-182.
22. E. Zio, "A Study of the Bootstrap Method for Estimating the Accuracy of Artificial Neural Networks in Predicting Nuclear Transient Processes", IEEE Transactions on Nuclear Science, Vol. 53, No. 3, 2006, pp. 1460-1478.
23. J. T. G. Huang, A. A. Ding, "Prediction intervals for artificial neural networks", Journal of the American Statistical Association, Vol. 92, 1997, pp. 748-757.
24. G. Chryssoloiuris, M. Lee,. A. Ramsey, "Confidence interval prediction for neural network models", IEEE Transactions on Neural Networks, Vol. 7, No. 1, 1996, pp. 229 - 232.
25. R. Wehrens, H. Putter, L. M. C. Buydens, "The bootstrap: a tutorial", Chemometrics and Intelligent Laboratory Systems, Vol. 54, 2000, pp. 35-52.
26. R. Dybowski, S. J. Roberts, "Confidence and prediction intervals for feed-forward neural networks", In Clinical Applications of Artificial Neural Networks, R. Dybowski and V. Gant, Eds. Cambridge, MA: Cambridge Univ. Press, 2000.