Multi-Dimensional Global Approximation Method Based Improved MARS

Article Preview

Abstract:

Global approximation for a complex “black-box” model (like a simulation model) with large domain or multi-dimensions can be applied in many fields such as parameter experiment, sensibility analysis, real-time simulation, and design/control optimization. For multi-dimensional global approximation, MARS (multi-variant adaptive regression splines) has unquestionable predominance over other common-used metamodel techniques. However, MARS has its own inevitable drawbacks which limit the range of its applications. This paper proposes a multi-dimensional global approximation method based improved MARS .Some tests and applications are given to prove the performance of the method.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 655-657)

Pages:

1005-1008

Citation:

Online since:

January 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Gu JC, Li GY, Dong ZM. Hybrid and Adaptive Metamodel Based Global Optimization. Proceedings of the ASME 2009 International Design Engineering Technical Conferences &Computers and Information in Engineering Conference, IDETC/CIE 2009, DETC2009-87121, August 30 - September 2, 2009, San Diego, California, USA.

DOI: 10.1115/detc2009-87121

Google Scholar

[2] Box GEP, Wilson KB. On the Experimental Attainment of Optimum Conditions. Journal of the Roal Statistical Society: Series B(Methodological) 1951; 13(1): 1-45.

Google Scholar

[3] Xuan S N, Alain SFD. Adaptive response surface method based on a double weighted regression technique. Probabilistic Engineering Mechanics 2009; 24: 135–143.

DOI: 10.1016/j.probengmech.2008.04.001

Google Scholar

[4] Shahsavani D, Grimvall A. An adaptive design and interpolation technique for extracting highly nonlinear response surfaces from deterministic models. Reliability Engineering and System Safety 2009; 94: 1173–1182.

DOI: 10.1016/j.ress.2008.10.013

Google Scholar

[5] James RS, Drew L, Rupert G et al. Adapting Second-order Response Surface Designs to Specific Need. Quality and Reliablity Engineering International 2008; 24: 331–349.

Google Scholar

[6] Jay DM. Computational Improvements to Estimating Kriging Metamodel Parameters. Journal of Mechanical Design 2009; 131.

Google Scholar