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Implicit estimation of Wiener series

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

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Zitation

Franz, M., & Schölkopf, B. (2004). Implicit estimation of Wiener series. In Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop (pp. 735-744).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-F397-9
Zusammenfassung
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.