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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

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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}
}