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Online Stabilization of Chaotic Maps Via Support Vector Machines Based Generalized Predictive Control

  • Serdar Iplikci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

In this study, the previously proposed Online Support Vector Machines Based Generalized Predictive Control method [1] is applied to the problem of stabilizing discrete-time chaotic systems with small parameter perturbations. The method combines the Accurate Online Support Vector Regression (AOSVR) algorithm [2] with the Support Vector Machines Based Generalized Predictive Control (SVM-Based GPC) approach [3] and thus provides a powerful scheme for controlling chaotic maps in an adaptive manner. The simulation results on chaotic maps have revealed that Online SVM-Based GPC provides an excellent online stabilization performance and maintains it when some measurement noise is added to output of the underlying map.

Keywords

Support Vector Machine Chaotic System Model Predictive Control Training Point Noisy Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Serdar Iplikci
    • 1
  1. 1.Department of Electrical and Electronics EngineeringPamukkale UniversityDenizliTurkey

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