On the simplification of an examples-based controller with support vector machines

A. Shmilovici, G. H. Bakir, A. Figueras, J. Lluís De La Rosa

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Examples-based controllers use historical data to evaluate local approximation models. Large data sets make it prohibitively expensive to evaluate the best control action in real time. Support vector machines (SVM) are known for their ability to identify the minimal set of data points needed to reconstruct an optimal decision surface. A successful application is presented: the simplification of a six-dimensional robotic controller. The SVM reduced the size of the data set to 5.3% of its original size while retaining 99.7% classification accuracy, thus leading the way to online implementation. The results indicate that SVM may be highly effective for the simplification of examples-based controllers.

Original languageEnglish
Pages (from-to)73-78
Number of pages6
JournalControl and Intelligent Systems
Volume35
Issue number1
StatePublished - 30 Mar 2007

Keywords

  • Machine learning
  • Multidimensional control
  • Support vector machine

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