On-line Nonparametric Regression to learn State-Dependent Disturbances

A combination of recursive least squares and weighted least squares is made which can adapt its structure such that a relation between inand 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. Keywords-Function approximation, Least Squares, On-line structure adaption, Non-parametric regression