Implicit estimation of Wiener series
2004
Conference Paper
ei
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 |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop |
Address: | New York |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{2643, title = {Implicit estimation of Wiener series}, author = {Franz, MO. and Sch{\"o}lkopf, B.}, journal = {Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop}, 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}, doi = {} } |