Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004.
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 crosscorrelation 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,doi:10.1109/mlsp.2004.1423040 fatcat:6jjcloppg5cd7d2v7sjrliy7si