Brief paper: Making parametric Hammerstein system identification a linear problem
Automatica (Journal of IFAC)
Blind identification of linear subsystems of LTI-ZMNL-LTI modelswith cyclostationary inputs
IEEE Transactions on Signal Processing
Identification of systems containing linear dynamic and static nonlinear elements
Automatica (Journal of IFAC)
Brief Identification of linear systems with hard input nonlinearities of known structure
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.