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Conference Paper

Implicit estimation of Wiener series

MPS-Authors
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Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Franz, M., & Schölkopf, B. (2004). Implicit estimation of Wiener series. In A. Barros, J. Principe, J. Larsen, T. Adali, & S. Douglas (Eds.), 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing 2004 (pp. 735-744). Piscataway, NJ, USA: IEEE Operations Center.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F397-9
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.