Online Stabilization of Chaotic Maps Via Support Vector Machines Based Generalized Predictive Control
In this study, the previously proposed Online Support Vector Machines Based Generalized Predictive Control method  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  with the Support Vector Machines Based Generalized Predictive Control (SVM-Based GPC) approach  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.
KeywordsSupport Vector Machine Chaotic System Model Predictive Control Training Point Noisy Condition
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