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Implicit estimation of Wiener series
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.
@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.} }