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.
Original language | English |
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Title of host publication | Proceedings of the 41st IEEE Conference on Decision and Control |
Place of Publication | Las Vegas, NV |
Publisher | IEEE |
Pages | 1343-1348 |
Number of pages | 6 |
Volume | 2 |
ISBN (Print) | 0-7803-7517-3, 0-7803-7516-5 |
DOIs | |
Publication status | Published - 10 Dec 2002 |
Event | 41st IEEE Conference on Decision and Control, CDC 2002 - Las Vegas, United States Duration: 10 Dec 2002 → 13 Dec 2002 Conference number: 41 |
Publication series
Name | Proceedings IEEE Conference on Decision and Control |
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Publisher | IEEE |
Volume | 2002 |
ISSN (Print) | 0191-2216 |
Conference
Conference | 41st IEEE Conference on Decision and Control, CDC 2002 |
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Abbreviated title | CDC |
Country/Territory | United States |
City | Las Vegas |
Period | 10/12/02 → 13/12/02 |