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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.
