References
- S. Rippa, "An algorithm for selecting a good value for the parameter c in radial basis function interpolation," Advances in Computational Mathematics, vol. 11, pp. 193-210, 1999 https://doi.org/10.1023/A:1018975909870
- L. Wang and D. Lowther, "Selection of approximation models for electromagnetic device optimization," IEEE Trans. on Magn., vol. 42, no. 4, pp. 1227-1230, Apr. 2006 https://doi.org/10.1109/TMAG.2006.871954
- S. L. Ho et al., "A RSM based on improved compactly supported radial bases function and its application to rapid optimizations of electromagnetic devices," IEEE Trans. on Magn., vol. 41, no. 6, pp. 2111-2117, Jun. 2005 https://doi.org/10.1109/TMAG.2005.848610
- J. Webb, "Construction of device performance models using adaptive interpolation and sensitivities," IEEE Trans. on Magn., vol. 41, no. 5, pp. 1768-1771, May 2005 https://doi.org/10.1109/TMAG.2005.845997
- X. K. Gao et al., "Robust design for torque optimization using response surface methodology," IEEE Trans. on Magn., vol. 38, no. 2, pp. 1141-1144, Mar. 2002 https://doi.org/10.1109/20.996292
- D. Han and A. Chatterjee, "Adaptive response surface modeling-based method for analog circuit sizing," Proceedings of IEEE International SOC Conference, 2004, pp. 109-112
-
Y. Yao, C. S. Koh, S. Yang, and G. Ni, "A Global Optimization algorithm based on
$C^1$ piecewise response surface patches," IEEE Trans. on Magn., vol. 43, no. 4, pp. 1629-1632, Apr. 2007 https://doi.org/10.1109/TMAG.2007.892484 - S. L. Ho, P. H. Ni, S. Y. Yang, and K. F. Wong, "An adaptive interpolating moving least squares response surface model applied to the design optimizations of electromagnetic devices," Proc. of CEFC, 2006, pp. 59
- J. R. Koehler and A. B. Owen, Computer Experiments, Handbook of Statistics, Elsevier Science, New York, 1996, pp. 261-308
- B. Brandstaetter, "SMES optimization benchmark, TEAM problem 22, 3 parameter problem," http://www.igte.tu-graz.ac.at/archive/team_new/team3.php
- R. H. C. Takahashi, J. A. Vasconcelos, J. A. Ramirez, and L. Krahenbuhl, "A multiobjective methodology for evaluating genetic operators," IEEE Trans. Magn., vol. 39, pp. 1321-1324, May 2003 https://doi.org/10.1109/TMAG.2003.810371
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