EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 42 (2016) MATEC Web of Conferences, 42 (2016) 05003 References
Open Access
Issue
MATEC Web of Conferences
Volume 42, 2016
2015 The 3rd International Conference on Control, Mechatronics and Automation (ICCMA 2015)
Article Number 05003
Number of page(s) 5
Section Applications of Computer and IT
DOI https://doi.org/10.1051/matecconf/20164205003
Published online 17 February 2016
  1. R. H. Myers, D. C. Montgomery and C. M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization using Designed Experiments, John Wiley & Sons., 2009 [Google Scholar]
  2. Gosavi, “Parametric Optimization: Response Surfaces And Neural Networks,” in Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, Rolla, Missouri, USA, Springer, 2015, pp. 37–46. [Google Scholar]
  3. A. Cameron, A. G. Windmeijer, H. Gramajo, D. Cane and C. Khosla, “An R-squared measure of goodness of fit for some common nonlinear regression models,” Journal of Econometrics, vol. 77, no. 2, pp. 329–342, 1997. [CrossRef] [Google Scholar]
  4. J. P. Kleijnen and R. G. Sargent, “A methodology for fitting and validating metamodels in Simulation,” European Journal of Operation Research, pp. 14–29, 2000. [CrossRef] [Google Scholar]
  5. S. Weisberg, Applied Linear Regression, Wiley, 2005. [CrossRef] [Google Scholar]
  6. G. G. Wang, Z. Dong and P. Aitchison, “Adaptative Response Surface Method – A Global Optimization Scheme for Approximation-based Design Problems,” Journal of Engineering Optimization, pp. 707–734, 2001. [CrossRef] [Google Scholar]
  7. G. G. Wang, “Adaptive response surface method using inherited Latin hypercube design points,” ASME Journal of Mechanical Design, vol. 1, nº 125, pp. 210–220, 2003. [Google Scholar]
  8. G. Steenackers, F. Presezniak and P. Guillaume, “Development of an adaptive response surface method for optimization of computation-intensive models,” Computers & Industrial Engineering, Elsevier, pp. 847–855, 2009. [CrossRef] [Google Scholar]
  9. L. Xiaojia y N. Fangfei, “New response surface model and its applications in aerodynamic optimization of axial compressor blade profile” Front. Energy Power Eng. China, vol. 2, nº 4, pp. 541–549, 2008. [CrossRef] [Google Scholar]
  10. J. Lee, S. Shin y S. Kim, “An Optimal Design of Marine Systems based on Neuro-Response Surface Method,” de 10th International Conference on Natural Computation, Xiamen, 2015. [Google Scholar]
  11. N. Roussouly, F. Petitjean y M. Salaun, “A new adaptive response surface method for reliability analysis” Probabilistic Engineering Mechanics, vol. 1, nº 32, pp. 103–115, 2013. [CrossRef] [Google Scholar]
  12. S. Chakraborty y S. Arunabh, “Adaptive response surface based efficient Finite Element Model Updating” Finite Elements in Analysis and Design, vol. 1, nº 80, pp. 33–40, 2014. [Google Scholar]
  13. X. S. Nguyen, A. Sellier, F. Duprat y G. Pons, “Adaptive response surface method based on a double weighted regression technique” Probabilistic Engineering Mechanics, vol. 1, nº 24, pp. 135–143, 2009. [CrossRef] [Google Scholar]
  14. W. Y. L. G. Hu y Z. Hua, “Optimization of sheet metal forming processes by adaptive response surface based on intelligent sampling method” journal of materials processing technology, vol. 1, nº 197, pp. 77–88, 2008. [Google Scholar]
  15. T. Santner, B. Williams and N. W., The Design and Analysis of Computer Experiments, Springer, 2003. [Google Scholar]
  16. P. Bratley and B. L. Fox, “Algorithm 659 Implementing Sobol’s Quasirandom Sequence Generator” ACM Transactions on Mathematical Software, vol. 14, no. 1, pp. 88–100, 1988. [CrossRef] [Google Scholar]
  17. J. C. Lagarias, J. A. Reeds, M. H. Wright and P. E. Wright, “Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions” SIAM Journal of Optimization, vol. 9, no. 1, pp. 112–147, 1998. [Google Scholar]
  18. G. Mastinu, M. Gobbi and C. Miano, Optimal Design of Complex Mechanical Systems with Applications to Vehicle Engineering, Springer, 2006. [Google Scholar]
  19. P. Y. Papalambros and D. J. Wilde, Principles of Optimal Design: Modeling and Computation, Cambridge University Press, 2000. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.