Name
Abstract | ||
|---|---|---|
We consider the problem of Wiener system identification in this note. A Wiener system consists of a linear time invariant block followed by a memoryless nonlinearity. By modeling the inverse of the memoryless nonlinearity as a linear combination of known nonlinear basis functions, we develop two subspace based approaches, namely an alternating projection algorithm and a minimum norm method, to solve for the Wiener system parameters. Based on computer simulations, the algorithms are shown to be robust in the presence of modeling error and noise. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TAC.2005.856662 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
System identification,Biological system modeling,Power system modeling,Cost function,Inverse problems,Projection algorithms,Computer simulation,Noise robustness,Computer errors,Nonlinear systems | Wiener filter,Wiener process,Integral representation theorem for classical Wiener space,Mathematical optimization,Dykstra's projection algorithm,Linear system,Control theory,Wiener deconvolution,Nonlinear system identification,Classical Wiener space,Mathematics | Journal |
Volume | Issue | ISSN |
50 | 10 | 0018-9286 |
Citations | PageRank | References |
16 | 0.90 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Raich | 1 | 197 | 19.98 |
| G. T. Zhou | 2 | 272 | 27.48 |
| M. Viberg | 3 | 917 | 188.13 |