Using a simulated annealing to enhance learning in adjustment processes

Albert Samà, Francisco J. Ruiz, Núria Agell, Cecilio Angulo

Research output: Book chapterConference contributionpeer-review

Abstract

This paper introduces a new approach to enhance learning in adjustment processes by using a support vector machine (SVM) algorithm as discriminant function jointly with an action generator module. The method trains a SVM with state-action patterns and uses trained SVM to select an appropriate action given a certain state in order to reach the target state. The system incorporates a simulated annealing technique to increase the exploration capacity and improve the ability to avoid local minima. The methodology has been tested in an example with artificial data.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages119-127
Number of pages9
Edition1
ISBN (Print)9781607500612
DOIs
Publication statusPublished - 2009
Externally publishedYes

Publication series

NameFrontiers in Artificial Intelligence and Applications
Number1
Volume202
ISSN (Print)0922-6389

Keywords

  • Adjustment process
  • Learning Algorithms
  • Machine Learning
  • Simulated Annealing
  • Support Vector Machines

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