Using a simulated annealing to enhance learning in adjustment processes

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. © 2009 The authors and IOS Press.

Año de publicación:

2009

Keywords:

• Machine learning
• SUPPORT VECTOR MACHINES
• Simulated Annealing
• Adjustment process
• learning algorithms

Fuente:

scopus