An improved adaptive sampling scheme for the construction of explicit boundaries

Anirban Basudhar, Samy Missoum

Research output: Contribution to journalArticle

86 Scopus citations

Abstract

This article presents an improved adaptive sampling scheme for the construction of explicit decision functions (constraints or limit state functions) using Support Vector Machines (SVMs). The proposed work presents substantial modifications to an earlier version of the scheme (Basudhar and Missoum, Comput Struct 86(19-20):1904-1917, 2008). The improvements consist of a different choice of samples, a more rigorous convergence criterion, and a new technique to select the SVM kernel parameters. Of particular interest is the choice of a new sample chosen to remove the "locking" of the SVM, a phenomenon that was not understood in the previous version of the algorithm. The new scheme is demonstrated on analytical problems of up to seven dimensions.

Original languageEnglish (US)
Pages (from-to)517-529
Number of pages13
JournalStructural and Multidisciplinary Optimization
Volume42
Issue number4
DOIs
StatePublished - Oct 1 2010

Keywords

  • Adaptive sampling
  • Decision boundaries
  • Support Vector Machines

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization

Fingerprint Dive into the research topics of 'An improved adaptive sampling scheme for the construction of explicit boundaries'. Together they form a unique fingerprint.

  • Cite this