RESEARCH COMMONS
LIBRARY

Structural Optimization Using Femlab And Smooth Support Vector Regression

ResearchCommons/Manakin Repository

Structural Optimization Using Femlab And Smooth Support Vector Regression

Show simple item record

dc.contributor.author Odapally, Divija en_US
dc.date.accessioned 2007-08-23T01:56:18Z
dc.date.available 2007-08-23T01:56:18Z
dc.date.issued 2007-08-23T01:56:18Z
dc.date.submitted May 2006 en_US
dc.identifier.other DISS-1302 en_US
dc.identifier.uri http://hdl.handle.net/10106/217
dc.description.abstract In recent years support vector machine (SVM) has been emerging as a popular tool for function approximation. Application of SVM for approximation of mathematical functions and complex engineering analysis has been represented by Palancz et al and Clarke et al, respectively. However the training of the original SVM involves the solution of a quadratic programming (QP) problem. This makes the application of SVM to large problem computationally expensive. To circumvent this difficulty, Lee et al developed a more efficient SVM formulation namely epsilon-SSVR which drastically improved the training efficiency of SVM. In this research the SSVR is used to build a metamodel for structural optimization. In the proposed method, Quasi Monte Carlo (QMC) technique is used for the selection of training data in the design space. SSVR using a radial basis function kernel is used to build the metamodel for structural optimization. The structural responses are evaluated by a commercial finite element package, FEMLAB (recently renamed as COMSOL). Several structural optimization examples are presented to illustrate the effectiveness of the proposed approach. en_US
dc.description.sponsorship Wang, Bo Ping en_US
dc.language.iso EN en_US
dc.publisher Mechanical Engineering en_US
dc.title Structural Optimization Using Femlab And Smooth Support Vector Regression en_US
dc.type M.S.M.E. en_US
dc.contributor.committeeChair Wang, Bo Ping en_US
dc.degree.department Mechanical Engineering en_US
dc.degree.discipline Mechanical Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level masters en_US
dc.degree.name M.S.M.E. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=275
dc.identifier.externalLinkDescription Link to Research Profiles

Files in this item

Files Size Format View
umi-uta-1302.pdf 780.2Kb PDF View/Open
780.2Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Browse

My Account

Statistics

About Us