Application of machine learning methods to cost estimation of product life cycle

International Journal of Computer Integrated Manufacturing

Volumn 25, Issue 4-5, 2012, Pages 340-352

  Yeh, Tsung Han ^a   Deng, S ^a  

^a NATIONAL DEFENSE UNIVERSITY   (Taiwan)

Author keywords

back propagation neural networks; cost estimation; data transformation; least squares support vector machines; statistical regression analysis

Indexed keywords

BACKPROPAGATION; COMPETITION; COST ACCOUNTING; COST BENEFIT ANALYSIS; COST ESTIMATING; DATA FLOW ANALYSIS; LEAST SQUARES APPROXIMATIONS; MACHINE LEARNING; METADATA; NEURAL NETWORKS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

BACK PROPAGATION NEURAL NETWORKS; COST ESTIMATIONS; DATA TRANSFORMATION; LEAST SQUARES SUPPORT VECTOR MACHINES; STATISTICAL REGRESSION ANALYSIS;

LIFE CYCLE;

EID: 84859727919     PISSN: 0951192X     EISSN: 13623052     Source Type: Journal    
DOI: 10.1080/0951192X.2011.645381     Document Type: Review

References (29)

1. Bode, J., 1997. Decision support with neural networks in the management of research and development: Concepts and application to cost estimation. Information and Management, 34, 33-40.
2. Bode, J., 2000. Neural networks for cost estimation: Simulations and pilot application. International Journal of Production Research, 38 (6), 1231-1254.
3. Deng, S., Chin, H., and Yeh, T.H., 2009. Using artificial neural networks to airframe wing structural design cost estimation. Journal of Chung Cheng Institute of Technology, 38 (1), 97-106.
4. Deng, S. and Yeh, T.H., 2009. Applying machine learning methods to the airframe structural design cost estimation - A case study of wing-box. In: 19th Annual International INCOSE Symposium, 20-23 July, Singapore. Red Hook, NY: Curran Associates, Inc.
5. Duan, K., Keerthi, S.S., and Poo, A.N., 2003. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing, 50, 41-59.
6. Duran, O., Rodriguez, N., and Consalter, L.A., 2009.Neural networks for cost estimation of shell and tube heat exchangers. Expert Systems with Applications, 36, 7435-7440.
7. Garza, J. and Rouhana, K., 1995. Neural networks versus parameter-based application in cost estimating. Cost Engineering, 37 (2), 14-18.
8. Gunn, S., 1998. Support vector machines for classification and regression. Boston, MA: MIT Press.
9. Hagan, T., Demuth, H., and Beale, M., 1996. Neural network design. Boston, MA: PWS Publishing.
10. Haykin, S., 1999. Neural networks - A comprehensive foundation. 2nd ed. Singapore: Prentice Hall.
11. International Society of Parametric Analysis (ISPA). 2008.
12. Jolai, F. and Ghanbari, A., 2010. Integrating data transformation techniques with Hopfield neural networks for solving travelling salesman problem. Expert Systems with Applications, 37, 5331-5335.
13. Layer, A., et al., 2002. Recent and future trends in cost estimation. International Journal of Computer Integrated Manufacturing, 15 (6), 499-510.
14. Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP MAGAZINE, 4, 4-22.
15. Mamdouh, R., 2007. Data preparation for data mining using SAS. San Francisco, CA: Morgan Kaufmann Publishers.
16. Montgomery, D.C., 2001. Design and analysis of experiments. 5th ed. New York: Wiley.
17. Prasad, P.W.C. and Beg, A., 2009. Investigating data preprocessing methods for circuit complexity models. Expert Systems with Applications, 36, 519-526.
18. Shtub, A. and Versano, R., 1999. Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis. International Journal of Production Economics, 62, 201-207.
19. Sigurdsen, A., 1992. CERA: An integrated cost estimating program. Cost Engineering, 34 (6), 25-30.
20. Smith, A.E. and Mason, A.K., 1997. Cost estimation predictive modeling: Regression versus neural network. The Engineering Economist, 42 (2), 137-161.
21. Smola, A.J. and Schö lkopf, B., 2002. Learning with kernels - support vector machines, regularization, optimization, and beyond. Boston, MA: MIT Press.
22. Suykens, J.A.K., et al., 2002. Least square support vector machines. Singapore: World Scientific.
23. Tay, E.H. and Cao, L., 2001. Application of support vector machines in financial time series forecasting. Omega, 29, 309-317.
24. Vapnik, V., 1995. The nature of statistical learning theory. New York: Springer.
25. Verlinden, B., et al., 2008. Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study. International Journal of Production Economics, 111, 484-492.
26. Wang, H.S., 2007. Application of BPN with featurebased models on cost estimation of plastic injection products. Computers and Industrial Engineering, 53, 79-94.
27. Yale, K., 1997. Preparing the right data for training neural networks. IEEE Spectrum, 34 (3), 64-66.
28. Younossi, O., Kennedy, M., and Graser, J.C., 2001. Military airframe cost - the effects of advanced materials and manufacturing processes - MR1370. Santa Monica, CA: RAND.
29. Zhang, Y.F. and Fuh, J.Y.H., 1998. A neural network approach for early cost estimation of packaging products. Computers and Industrial Engineering, 34 (2), 433-450.