The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based on regression in a reproducing kernel Hilbert space that alleviates these problems. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.
| Author(s): | Franz, MO. and Schölkopf, B. |
| Journal: | Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop |
| Pages: | 735-744 |
| Year: | 2004 |
| Day: | 0 |
| Editors: | A Barros and J Principe and J Larsen and T Adali and S Douglas |
| Publisher: | IEEE |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | New York |
| Event Name: | Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{2643,
title = {Implicit estimation of Wiener series},
journal = {Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop},
abstract = {The Wiener series is one of the standard methods to systematically
characterize the nonlinearity of a system. The classical estimation
method of the expansion coefficients via cross-correlation suffers
from severe problems that prevent its application to high-dimensional
and strongly nonlinear systems. We propose an implicit estimation
method based on regression in a reproducing kernel Hilbert space that
alleviates these problems. Experiments show performance advantages in
terms of convergence, interpretability, and system sizes that can be
handled.},
pages = {735-744},
editors = {A Barros and J Principe and J Larsen and T Adali and S Douglas},
publisher = {IEEE},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {New York},
year = {2004},
slug = {2643},
author = {Franz, MO. and Sch{\"o}lkopf, B.}
}