On-line nonparametric regression to learn state-dependent disturbances

Kruif de, Bas J. and Vries de, Theo J.A. (2003) On-line nonparametric regression to learn state-dependent disturbances. In: IEEE International Symposium on Intelligent Control, 2003, October 5-8, 2003, Houston, Texas.

PDF
428Kb

Abstract:	A combination of recursive least squares and weighted least squares is made which can adapt its structure such that a relation between in- and output can he approximated, even when the structure of this relation is unknown beforehand. This method can adapt its structure on-line while it preserves information offered by previous samples, making it applicable in a control setting. This method has been tested with compntergenerated data, and it b used in a simulation to learn the non-linear state-dependent effects, both with good success.
Item Type:	Conference or Workshop Item
Copyright:	© 2003 IEEE
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Robotics and Mechatronics (CE)
Link to this item:	http://purl.utwente.nl/publications/74467
Official URL:	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1253917
Export this item as:	BibTeX EndNote HTML Citation Reference Manager

Show download statistics for this publication

Repository Staff Only: item control page

Metis ID: 213834