Support-vector-based least squares for learning non-linear dynamics

Kruif de, Bas J. and Vries de, Theo J.A. (2002) Support-vector-based least squares for learning non-linear dynamics. In: 41st IEEE Conference on Decision and Control, 2002, 10-13 Dec. 2002, Las Vegas, Nevada, U.S.A.

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Abstract:	A function approximator is introduced that is based on least squares support vector machines (LSSVM) and on least squares (LS). The potential indicators for the LS method are chosen as the kernel functions of all the training samples similar to LSSVM. By selecting these as indicator functions the indicators for LS can be interpret in a support vector machine setting and the curse of dimensionality can be circumvented. The indicators are included by a forward selection scheme. This makes the computational load for the training phase small. As long as the function is not approximated good enough, and the function is not overfitting the data, a new indicator is included. To test the approximator the inverse nonlinear dynamics of a linear motor are learnt. This is done by including the approximator as learning mechanism in a learning feedforward controller.
Item Type:	Conference or Workshop Item
Copyright:	©2002 IEEE
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Robotics and Mechatronics (CE)
Link to this item:	http://purl.utwente.nl/publications/43860
Official URL:	http://dx.doi.org/10.1109/CDC.2002.1184702
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