Title :
Implicit estimation of wiener series
Author :
Franz, Matthias O. ; Schölkopf, Bemhard
Author_Institution :
Max-Planck-Inst. fur Biol. Kybernetik, Tubingen
fDate :
Sept. 29 2004-Oct. 1 2004
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 prevents its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based on regression in a reproducing kernel Hubert space that alleviates these problems. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled
Keywords :
nonlinear systems; regression analysis; series (mathematics); signal processing; Wiener series; implicit estimation method; regression; reproducing kernel Hubert space; system nonlinearity; Biomedical signal processing; Convergence; Gaussian noise; Hilbert space; Kernel; Linear systems; Neuroscience; Nonlinear systems; Signal processing; System identification;
Conference_Titel :
Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop
Conference_Location :
Sao Luis
Print_ISBN :
0-7803-8608-4
DOI :
10.1109/MLSP.2004.1423040