Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2004 | ESANN | Applied mathematics,Pattern recognition,Multivariate statistics,Algorithm,Artificial intelligence,Functional equation,Linearization,Mathematics,Bounded function |
DocType | Citations | PageRank |
Conference | 1 | 0.38 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian J. Theis | 1 | 931 | 85.37 |
| Elmar Wolfgang Lang | 2 | 260 | 36.10 |