J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

P. K. Wong, Q. S. Xu, C. M. Vong, and H. Ch. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Trans. Ind. Electron. 59, 1988–2001 (2012).

[CrossRef]

F. Bellocchio, S. Ferrari, V. Piuri, and N. A. Borghese, “Hierarchical approach for multiscale support vector regression,” IEEE Trans. Neural Netw. Learn. Syst. 23, 1448–1460 (2012).

[CrossRef]

M. Gönen and E. Alpaydim, “Multiple kernel learning algorithms,” J. Mach. Learn. Res. 12, 2211–2268 (2011).

G. J. Michels and V. L. Genberg, “Optomechanical analysis and design tool for adaptive x-ray optics,” Proc. SPIE 7803, 780308 (2010).

[CrossRef]

H. Wang, K. Rong, H. Li, and P. Shaun, “Computer aided fixture design: recent research and trends,” Comput. Aided Des. 42, 1085–1094 (2010).

[CrossRef]

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

S. B. Qiu and T. Lane, “A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction,” IEEE/ACM Trans. Comput. Biol. Bioinform. 6, 90–199 (2009).

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

H. Chang, Z. G. Fan, S. Q. Chen, and Y.-M. Cao, “Impact of the temperature gradient on optical system parameters: modeling and analysis,” Proc. SPIE 7506, 75060G (2009).

M. K. Cho, “Performance prediction of the TMT tertiary mirror support system,” Proc. SPIE 7018, 70184F (2008).

[CrossRef]

Y. H. Liu, H. P. Huang, and C. H. Weng, “Recognition of electromyographic signals using cascaded kernel learning machine,” IEEE/ASME Trans. Mechatron. 12, 253–264 (2007).

[CrossRef]

L. P. Zhou and D. W. Tang, “A functionally graded structural design of mirrors for reducing their thermal deformations in high-power laser systems by finite element method,” Opt. Laser Technol. 39, 980–986 (2007).

[CrossRef]

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

M. K. Cho, R. S. Price, and I. K. Moon, “Optimization of the ATST primary mirror support system,” Proc. SPIE 6273, 62731E (2006).

[CrossRef]

V. L. Genberg, G. J. Michels, and K. B. Doyle, “Design optimization of actuator layouts of adaptive optics using a genetic algorithm,” Proc. SPIE 5877, 58770L (2005).

R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005).

[CrossRef]

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

K. B. Doyle, V. L. Genberg, and G. J. Michels, “Integrated optomechanical analysis of adaptive optical systems,” Proc. SPIE 5178, 20–28 (2004).

[CrossRef]

D. Bi, Y. F. Li, S. K. Tso, and G. L. Wang, “Friction modeling and compensation for haptic display based on support vector machine,” IEEE Trans. Ind. Electron. 51, 491–500 (2004).

[CrossRef]

G. Wang, Y. Li, and D. Bi, “Support vector machine networks for friction modeling,” IEEE/ASME Trans. Mechatron. 9, 601–606 (2004).

[CrossRef]

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput 14, 199–222 (2004).

[CrossRef]

J. H. Lee, T. K. Uhm, W. S. Lee, and S. K. Youn, “First order analysis of thin plate deformable mirrors,” J. Korean Phys. Soc. 44, 1412–1416 (2004).

Y. Li and D. Bi, “A method for dynamics identification for haptic display of the operating feel in virtual environments,” IEEE/ASME Trans. Mechatron. 8, 476–482 (2003).

[CrossRef]

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).

[CrossRef]

L. Daudeville and H. Carre, “Thermal tempering simulation of glass plates: Inner and edge residual stresses,” J. Therm. Stress. 21, 667–689 (1998).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

G. L. Herrit and H. E. Reedy, “Advanced figure of merit evaluation for CO2 laser optics using finite element analysis,” Proc. SPIE 1047, 33–42 (1989).

[CrossRef]

R. Pelossof, A. Miller, P. Allen, and T. Jebara, “A SVM learning approach to robotic grasping,” in IEEE International Conference on Robotics and Automation (2004), Vol. 4, pp. 3512–3518.

M. Gönen and E. Alpaydim, “Multiple kernel learning algorithms,” J. Mach. Learn. Res. 12, 2211–2268 (2011).

M. Gönen and E. Alpaydin, “Localized multiple kernel learning,” in Proceedings 25th International Conference on Machine Learning (2008), pp. 352–359.

M. Gönen and E. Alpaydin, “Localized multiple kernel regression,” in Proceedings 20th IAPR International Conference on Pattern Recognition (2010), pp. 1425–1428.

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

F. Bellocchio, S. Ferrari, V. Piuri, and N. A. Borghese, “Hierarchical approach for multiscale support vector regression,” IEEE Trans. Neural Netw. Learn. Syst. 23, 1448–1460 (2012).

[CrossRef]

D. Bi, Y. F. Li, S. K. Tso, and G. L. Wang, “Friction modeling and compensation for haptic display based on support vector machine,” IEEE Trans. Ind. Electron. 51, 491–500 (2004).

[CrossRef]

G. Wang, Y. Li, and D. Bi, “Support vector machine networks for friction modeling,” IEEE/ASME Trans. Mechatron. 9, 601–606 (2004).

[CrossRef]

Y. Li and D. Bi, “A method for dynamics identification for haptic display of the operating feel in virtual environments,” IEEE/ASME Trans. Mechatron. 8, 476–482 (2003).

[CrossRef]

F. Bellocchio, S. Ferrari, V. Piuri, and N. A. Borghese, “Hierarchical approach for multiscale support vector regression,” IEEE Trans. Neural Netw. Learn. Syst. 23, 1448–1460 (2012).

[CrossRef]

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).

[CrossRef]

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

H. Chang, Z. G. Fan, S. Q. Chen, and Y.-M. Cao, “Impact of the temperature gradient on optical system parameters: modeling and analysis,” Proc. SPIE 7506, 75060G (2009).

L. Daudeville and H. Carre, “Thermal tempering simulation of glass plates: Inner and edge residual stresses,” J. Therm. Stress. 21, 667–689 (1998).

[CrossRef]

H. Chang, Z. G. Fan, S. Q. Chen, and Y.-M. Cao, “Impact of the temperature gradient on optical system parameters: modeling and analysis,” Proc. SPIE 7506, 75060G (2009).

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).

[CrossRef]

H. Chang, Z. G. Fan, S. Q. Chen, and Y.-M. Cao, “Impact of the temperature gradient on optical system parameters: modeling and analysis,” Proc. SPIE 7506, 75060G (2009).

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

M. K. Cho, “Performance prediction of the TMT tertiary mirror support system,” Proc. SPIE 7018, 70184F (2008).

[CrossRef]

M. K. Cho, R. S. Price, and I. K. Moon, “Optimization of the ATST primary mirror support system,” Proc. SPIE 6273, 62731E (2006).

[CrossRef]

J. Clausen, “Branch and bound algorithms-principles and examples,” in Parallel Computing in Optimization (Applied Optimization) (Springer, 1997), pp. 239–267.

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. 11, pp. 38–39.

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

L. Daudeville and H. Carre, “Thermal tempering simulation of glass plates: Inner and edge residual stresses,” J. Therm. Stress. 21, 667–689 (1998).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

J. M. Moguerza, A. Muñoz, and I. M. de Diego, “Improving support vector classification via the combination of multiple sources of information,” in Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops (2004).

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

V. L. Genberg, G. J. Michels, and K. B. Doyle, “Design optimization of actuator layouts of adaptive optics using a genetic algorithm,” Proc. SPIE 5877, 58770L (2005).

K. B. Doyle, V. L. Genberg, and G. J. Michels, “Integrated optomechanical analysis of adaptive optical systems,” Proc. SPIE 5178, 20–28 (2004).

[CrossRef]

J. J. Yang, Y. N. Li, Y. H. Tian, L. Y. Duan, and W. Gao, “Group-sensitive multiple kernel learning for object categorization,” in Proceedings of the 12th IEEE International Conference on Computer Vision (2009), pp. 436–443.

H. Chang, Z. G. Fan, S. Q. Chen, and Y.-M. Cao, “Impact of the temperature gradient on optical system parameters: modeling and analysis,” Proc. SPIE 7506, 75060G (2009).

F. Bellocchio, S. Ferrari, V. Piuri, and N. A. Borghese, “Hierarchical approach for multiscale support vector regression,” IEEE Trans. Neural Netw. Learn. Syst. 23, 1448–1460 (2012).

[CrossRef]

Y. Miyamoto, W. A. Kaysser, B. H. Rabin, A. Kawasaki, and R. G. Ford, Functionally Graded Materials: Design, Processing and Applications (Kluwer, 1999).

J. J. Yang, Y. N. Li, Y. H. Tian, L. Y. Duan, and W. Gao, “Group-sensitive multiple kernel learning for object categorization,” in Proceedings of the 12th IEEE International Conference on Computer Vision (2009), pp. 436–443.

G. J. Michels and V. L. Genberg, “Optomechanical analysis and design tool for adaptive x-ray optics,” Proc. SPIE 7803, 780308 (2010).

[CrossRef]

V. L. Genberg, G. J. Michels, and K. B. Doyle, “Design optimization of actuator layouts of adaptive optics using a genetic algorithm,” Proc. SPIE 5877, 58770L (2005).

K. B. Doyle, V. L. Genberg, and G. J. Michels, “Integrated optomechanical analysis of adaptive optical systems,” Proc. SPIE 5178, 20–28 (2004).

[CrossRef]

G. J. Michels and V. L. Genberg, “Advances in the analysis and design of adaptive optics,” in Imaging and Applied Optics, OSA Technical Digest (CD) (Optical Society of America, 2011), paper AMC2.

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

M. Gönen and E. Alpaydim, “Multiple kernel learning algorithms,” J. Mach. Learn. Res. 12, 2211–2268 (2011).

M. Gönen and E. Alpaydin, “Localized multiple kernel learning,” in Proceedings 25th International Conference on Machine Learning (2008), pp. 352–359.

M. Gönen and E. Alpaydin, “Localized multiple kernel regression,” in Proceedings 20th IAPR International Conference on Pattern Recognition (2010), pp. 1425–1428.

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

G. L. Herrit and H. E. Reedy, “Advanced figure of merit evaluation for CO2 laser optics using finite element analysis,” Proc. SPIE 1047, 33–42 (1989).

[CrossRef]

Y. H. Liu, H. P. Huang, and C. H. Weng, “Recognition of electromyographic signals using cascaded kernel learning machine,” IEEE/ASME Trans. Mechatron. 12, 253–264 (2007).

[CrossRef]

R. Pelossof, A. Miller, P. Allen, and T. Jebara, “A SVM learning approach to robotic grasping,” in IEEE International Conference on Robotics and Automation (2004), Vol. 4, pp. 3512–3518.

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

Y. Miyamoto, W. A. Kaysser, B. H. Rabin, A. Kawasaki, and R. G. Ford, Functionally Graded Materials: Design, Processing and Applications (Kluwer, 1999).

Y. Miyamoto, W. A. Kaysser, B. H. Rabin, A. Kawasaki, and R. G. Ford, Functionally Graded Materials: Design, Processing and Applications (Kluwer, 1999).

N. Kingsbury, D. B. H. Tay, and M. Palaniswami, “Multi-scale kernel methods for classification,” in Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (2005), pp. 43–48.

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

S. B. Qiu and T. Lane, “A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction,” IEEE/ACM Trans. Comput. Biol. Bioinform. 6, 90–199 (2009).

J. H. Lee, T. K. Uhm, W. S. Lee, and S. K. Youn, “First order analysis of thin plate deformable mirrors,” J. Korean Phys. Soc. 44, 1412–1416 (2004).

J. H. Lee, T. K. Uhm, W. S. Lee, and S. K. Youn, “First order analysis of thin plate deformable mirrors,” J. Korean Phys. Soc. 44, 1412–1416 (2004).

H. Wang, K. Rong, H. Li, and P. Shaun, “Computer aided fixture design: recent research and trends,” Comput. Aided Des. 42, 1085–1094 (2010).

[CrossRef]

G. Wang, Y. Li, and D. Bi, “Support vector machine networks for friction modeling,” IEEE/ASME Trans. Mechatron. 9, 601–606 (2004).

[CrossRef]

Y. Li and D. Bi, “A method for dynamics identification for haptic display of the operating feel in virtual environments,” IEEE/ASME Trans. Mechatron. 8, 476–482 (2003).

[CrossRef]

D. Bi, Y. F. Li, S. K. Tso, and G. L. Wang, “Friction modeling and compensation for haptic display based on support vector machine,” IEEE Trans. Ind. Electron. 51, 491–500 (2004).

[CrossRef]

J. J. Yang, Y. N. Li, Y. H. Tian, L. Y. Duan, and W. Gao, “Group-sensitive multiple kernel learning for object categorization,” in Proceedings of the 12th IEEE International Conference on Computer Vision (2009), pp. 436–443.

Y. H. Liu, H. P. Huang, and C. H. Weng, “Recognition of electromyographic signals using cascaded kernel learning machine,” IEEE/ASME Trans. Mechatron. 12, 253–264 (2007).

[CrossRef]

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

G. J. Michels and V. L. Genberg, “Optomechanical analysis and design tool for adaptive x-ray optics,” Proc. SPIE 7803, 780308 (2010).

[CrossRef]

V. L. Genberg, G. J. Michels, and K. B. Doyle, “Design optimization of actuator layouts of adaptive optics using a genetic algorithm,” Proc. SPIE 5877, 58770L (2005).

K. B. Doyle, V. L. Genberg, and G. J. Michels, “Integrated optomechanical analysis of adaptive optical systems,” Proc. SPIE 5178, 20–28 (2004).

[CrossRef]

G. J. Michels and V. L. Genberg, “Advances in the analysis and design of adaptive optics,” in Imaging and Applied Optics, OSA Technical Digest (CD) (Optical Society of America, 2011), paper AMC2.

R. Pelossof, A. Miller, P. Allen, and T. Jebara, “A SVM learning approach to robotic grasping,” in IEEE International Conference on Robotics and Automation (2004), Vol. 4, pp. 3512–3518.

Y. Miyamoto, W. A. Kaysser, B. H. Rabin, A. Kawasaki, and R. G. Ford, Functionally Graded Materials: Design, Processing and Applications (Kluwer, 1999).

J. M. Moguerza, A. Muñoz, and I. M. de Diego, “Improving support vector classification via the combination of multiple sources of information,” in Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops (2004).

M. K. Cho, R. S. Price, and I. K. Moon, “Optimization of the ATST primary mirror support system,” Proc. SPIE 6273, 62731E (2006).

[CrossRef]

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).

[CrossRef]

J. M. Moguerza, A. Muñoz, and I. M. de Diego, “Improving support vector classification via the combination of multiple sources of information,” in Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops (2004).

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

T. Ruppel, O. Sawodny, and W. Osten, “Actuator placement for minimum force modal control of continuous faceplate deformable mirrors,” in IEEE International Conference on Control Applications (2010), pp. 867–872.

N. Kingsbury, D. B. H. Tay, and M. Palaniswami, “Multi-scale kernel methods for classification,” in Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (2005), pp. 43–48.

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

R. Pelossof, A. Miller, P. Allen, and T. Jebara, “A SVM learning approach to robotic grasping,” in IEEE International Conference on Robotics and Automation (2004), Vol. 4, pp. 3512–3518.

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

F. Bellocchio, S. Ferrari, V. Piuri, and N. A. Borghese, “Hierarchical approach for multiscale support vector regression,” IEEE Trans. Neural Netw. Learn. Syst. 23, 1448–1460 (2012).

[CrossRef]

M. K. Cho, R. S. Price, and I. K. Moon, “Optimization of the ATST primary mirror support system,” Proc. SPIE 6273, 62731E (2006).

[CrossRef]

S. B. Qiu and T. Lane, “A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction,” IEEE/ACM Trans. Comput. Biol. Bioinform. 6, 90–199 (2009).

Y. Miyamoto, W. A. Kaysser, B. H. Rabin, A. Kawasaki, and R. G. Ford, Functionally Graded Materials: Design, Processing and Applications (Kluwer, 1999).

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

G. L. Herrit and H. E. Reedy, “Advanced figure of merit evaluation for CO2 laser optics using finite element analysis,” Proc. SPIE 1047, 33–42 (1989).

[CrossRef]

H. Wang, K. Rong, H. Li, and P. Shaun, “Computer aided fixture design: recent research and trends,” Comput. Aided Des. 42, 1085–1094 (2010).

[CrossRef]

T. Ruppel, O. Sawodny, and W. Osten, “Actuator placement for minimum force modal control of continuous faceplate deformable mirrors,” in IEEE International Conference on Control Applications (2010), pp. 867–872.

T. Ruppel, O. Sawodny, and W. Osten, “Actuator placement for minimum force modal control of continuous faceplate deformable mirrors,” in IEEE International Conference on Control Applications (2010), pp. 867–872.

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput 14, 199–222 (2004).

[CrossRef]

H. Wang, K. Rong, H. Li, and P. Shaun, “Computer aided fixture design: recent research and trends,” Comput. Aided Des. 42, 1085–1094 (2010).

[CrossRef]

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput 14, 199–222 (2004).

[CrossRef]

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

L. P. Zhou and D. W. Tang, “A functionally graded structural design of mirrors for reducing their thermal deformations in high-power laser systems by finite element method,” Opt. Laser Technol. 39, 980–986 (2007).

[CrossRef]

N. Kingsbury, D. B. H. Tay, and M. Palaniswami, “Multi-scale kernel methods for classification,” in Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (2005), pp. 43–48.

J. J. Yang, Y. N. Li, Y. H. Tian, L. Y. Duan, and W. Gao, “Group-sensitive multiple kernel learning for object categorization,” in Proceedings of the 12th IEEE International Conference on Computer Vision (2009), pp. 436–443.

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

D. Bi, Y. F. Li, S. K. Tso, and G. L. Wang, “Friction modeling and compensation for haptic display based on support vector machine,” IEEE Trans. Ind. Electron. 51, 491–500 (2004).

[CrossRef]

J. H. Lee, T. K. Uhm, W. S. Lee, and S. K. Youn, “First order analysis of thin plate deformable mirrors,” J. Korean Phys. Soc. 44, 1412–1416 (2004).

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).

[CrossRef]

P. K. Wong, Q. S. Xu, C. M. Vong, and H. Ch. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Trans. Ind. Electron. 59, 1988–2001 (2012).

[CrossRef]

G. Wang, Y. Li, and D. Bi, “Support vector machine networks for friction modeling,” IEEE/ASME Trans. Mechatron. 9, 601–606 (2004).

[CrossRef]

D. Bi, Y. F. Li, S. K. Tso, and G. L. Wang, “Friction modeling and compensation for haptic display based on support vector machine,” IEEE Trans. Ind. Electron. 51, 491–500 (2004).

[CrossRef]

H. Wang, K. Rong, H. Li, and P. Shaun, “Computer aided fixture design: recent research and trends,” Comput. Aided Des. 42, 1085–1094 (2010).

[CrossRef]

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

Y. H. Liu, H. P. Huang, and C. H. Weng, “Recognition of electromyographic signals using cascaded kernel learning machine,” IEEE/ASME Trans. Mechatron. 12, 253–264 (2007).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

P. K. Wong, Q. S. Xu, C. M. Vong, and H. Ch. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Trans. Ind. Electron. 59, 1988–2001 (2012).

[CrossRef]

P. K. Wong, Q. S. Xu, C. M. Vong, and H. Ch. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Trans. Ind. Electron. 59, 1988–2001 (2012).

[CrossRef]

R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005).

[CrossRef]

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. 11, pp. 38–39.

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

P. K. Wong, Q. S. Xu, C. M. Vong, and H. Ch. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Trans. Ind. Electron. 59, 1988–2001 (2012).

[CrossRef]

R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005).

[CrossRef]

J. J. Yang, Y. N. Li, Y. H. Tian, L. Y. Duan, and W. Gao, “Group-sensitive multiple kernel learning for object categorization,” in Proceedings of the 12th IEEE International Conference on Computer Vision (2009), pp. 436–443.

J. H. Lee, T. K. Uhm, W. S. Lee, and S. K. Youn, “First order analysis of thin plate deformable mirrors,” J. Korean Phys. Soc. 44, 1412–1416 (2004).

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

L. P. Zhou and D. W. Tang, “A functionally graded structural design of mirrors for reducing their thermal deformations in high-power laser systems by finite element method,” Opt. Laser Technol. 39, 980–986 (2007).

[CrossRef]

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

H. Q. Wang, F. C. Sun, Y. N. Cai, N. Cheng, and L. G. Ding, “On multiple kernel learning methods,” Acta Autom. Sinica 36, 1037–1050 (2010).

[CrossRef]

J. X. Yu, S. B. He, X. Xiang, X. D. Yuan, W. G. Zheng, H. B. Lv, and X. T. Zu, “High temperature thermal behaviour modeling of large-scale fused silica optics for laser facility,” Chin. Phys. B 21, 064401 (2012).

Y. Ning, H. Zhou, H. Yu, C. H. Rao, and W. H. Jiang, “Classical areas of phenomenology: thermal stability test and analysis of a 20-actuator bimorph deformable mirror,” Chin. Phys. B 18, 1089–1095 (2009).

H. Wang, K. Rong, H. Li, and P. Shaun, “Computer aided fixture design: recent research and trends,” Comput. Aided Des. 42, 1085–1094 (2010).

[CrossRef]

D. Bi, Y. F. Li, S. K. Tso, and G. L. Wang, “Friction modeling and compensation for haptic display based on support vector machine,” IEEE Trans. Ind. Electron. 51, 491–500 (2004).

[CrossRef]

P. K. Wong, Q. S. Xu, C. M. Vong, and H. Ch. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Trans. Ind. Electron. 59, 1988–2001 (2012).

[CrossRef]

R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005).

[CrossRef]

F. Bellocchio, S. Ferrari, V. Piuri, and N. A. Borghese, “Hierarchical approach for multiscale support vector regression,” IEEE Trans. Neural Netw. Learn. Syst. 23, 1448–1460 (2012).

[CrossRef]

S. B. Qiu and T. Lane, “A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction,” IEEE/ACM Trans. Comput. Biol. Bioinform. 6, 90–199 (2009).

Y. H. Liu, H. P. Huang, and C. H. Weng, “Recognition of electromyographic signals using cascaded kernel learning machine,” IEEE/ASME Trans. Mechatron. 12, 253–264 (2007).

[CrossRef]

G. Wang, Y. Li, and D. Bi, “Support vector machine networks for friction modeling,” IEEE/ASME Trans. Mechatron. 9, 601–606 (2004).

[CrossRef]

Y. Li and D. Bi, “A method for dynamics identification for haptic display of the operating feel in virtual environments,” IEEE/ASME Trans. Mechatron. 8, 476–482 (2003).

[CrossRef]

J. H. Lee, T. K. Uhm, W. S. Lee, and S. K. Youn, “First order analysis of thin plate deformable mirrors,” J. Korean Phys. Soc. 44, 1412–1416 (2004).

M. Gönen and E. Alpaydim, “Multiple kernel learning algorithms,” J. Mach. Learn. Res. 12, 2211–2268 (2011).

G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” J. Mach. Learn. Res. 5, 27–72 (2004).

L. Daudeville and H. Carre, “Thermal tempering simulation of glass plates: Inner and edge residual stresses,” J. Therm. Stress. 21, 667–689 (1998).

[CrossRef]

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).

[CrossRef]

L. P. Zhou and D. W. Tang, “A functionally graded structural design of mirrors for reducing their thermal deformations in high-power laser systems by finite element method,” Opt. Laser Technol. 39, 980–986 (2007).

[CrossRef]

Y. F. Peng, J. L. Cui, Z. H. Cheng, D. L. Zuo, and Y. N. Zhang, “Characteristics of thermal distortions of the laser mirror substrates filled with phase change materials,” Opt. Laser Technol. 38, 594–598 (2006).

[CrossRef]

K. B. Doyle, V. L. Genberg, and G. J. Michels, “Integrated optomechanical analysis of adaptive optical systems,” Proc. SPIE 5178, 20–28 (2004).

[CrossRef]

G. L. Herrit and H. E. Reedy, “Advanced figure of merit evaluation for CO2 laser optics using finite element analysis,” Proc. SPIE 1047, 33–42 (1989).

[CrossRef]

H. Chang, Z. G. Fan, S. Q. Chen, and Y.-M. Cao, “Impact of the temperature gradient on optical system parameters: modeling and analysis,” Proc. SPIE 7506, 75060G (2009).

V. L. Genberg, G. J. Michels, and K. B. Doyle, “Design optimization of actuator layouts of adaptive optics using a genetic algorithm,” Proc. SPIE 5877, 58770L (2005).

G. J. Michels and V. L. Genberg, “Optomechanical analysis and design tool for adaptive x-ray optics,” Proc. SPIE 7803, 780308 (2010).

[CrossRef]

M. K. Cho, “Performance prediction of the TMT tertiary mirror support system,” Proc. SPIE 7018, 70184F (2008).

[CrossRef]

H. M. Martin, S. P. Callahan, B. Cuerden, W. B. Davison, S. T. Derigne, L. R. Dettmann, G. Parodi, T. J. Trebisky, S. C. West, and J. T. Williams, “Active supports and force optimization for the MMT primary mirror,” Proc. SPIE 3352, 412–423 (1998).

[CrossRef]

M. K. Cho, R. S. Price, and I. K. Moon, “Optimization of the ATST primary mirror support system,” Proc. SPIE 6273, 62731E (2006).

[CrossRef]

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput 14, 199–222 (2004).

[CrossRef]

T. Ruppel, O. Sawodny, and W. Osten, “Actuator placement for minimum force modal control of continuous faceplate deformable mirrors,” in IEEE International Conference on Control Applications (2010), pp. 867–872.

R. Pelossof, A. Miller, P. Allen, and T. Jebara, “A SVM learning approach to robotic grasping,” in IEEE International Conference on Robotics and Automation (2004), Vol. 4, pp. 3512–3518.

G. J. Michels and V. L. Genberg, “Advances in the analysis and design of adaptive optics,” in Imaging and Applied Optics, OSA Technical Digest (CD) (Optical Society of America, 2011), paper AMC2.

J. Clausen, “Branch and bound algorithms-principles and examples,” in Parallel Computing in Optimization (Applied Optimization) (Springer, 1997), pp. 239–267.

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. 11, pp. 38–39.

Y. Miyamoto, W. A. Kaysser, B. H. Rabin, A. Kawasaki, and R. G. Ford, Functionally Graded Materials: Design, Processing and Applications (Kluwer, 1999).

N. Kingsbury, D. B. H. Tay, and M. Palaniswami, “Multi-scale kernel methods for classification,” in Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (2005), pp. 43–48.

J. J. Yang, Y. N. Li, Y. H. Tian, L. Y. Duan, and W. Gao, “Group-sensitive multiple kernel learning for object categorization,” in Proceedings of the 12th IEEE International Conference on Computer Vision (2009), pp. 436–443.

J. M. Moguerza, A. Muñoz, and I. M. de Diego, “Improving support vector classification via the combination of multiple sources of information,” in Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops (2004).

M. Gönen and E. Alpaydin, “Localized multiple kernel regression,” in Proceedings 20th IAPR International Conference on Pattern Recognition (2010), pp. 1425–1428.

M. Gönen and E. Alpaydin, “Localized multiple kernel learning,” in Proceedings 25th International Conference on Machine Learning (2008), pp. 352–359.