Implicit estimation of Wiener series
Matthias Franz and Bernhard Schölkopf
In: Machine Learning for Signal Processing XIV, 28 Sep - 01 Oct 2004, Sao Luis, Brasil.
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.