Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Accurate on-line support vector regression
Neural Computation
Applied Optimization with MATLAB Programming
Applied Optimization with MATLAB Programming
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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.