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 language | English (US) |
---|---|
Pages (from-to) | 517-529 |
Number of pages | 13 |
Journal | Structural and Multidisciplinary Optimization |
Volume | 42 |
Issue number | 4 |
DOIs | |
State | Published - Oct 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