Linearization identification and an application to BSS using a SOM
Fabian Joachim Theis, Elmar Wolfgang Lang · University of Regensburg Publication Server (University of Regensburg) · 2004
The one-dimensional functional equation g(y(t)) = cg(z(t)) with known functions y and z and constant c is considered. The indeter- minacies are calculated, and an algorithm for approximating g given y and z at finitely many time instants is proposed. This linearization iden- tification algorithm is applied to the postnonlinear blind source separa- tion (BSS) problem in the case of independent sources with bounded den- sities. A self-organizing map (SOM) is used to approximate the boundary, and the postnonlinearity estimation in this multivariate case is reduced to the one-dimensional functional equation from above.