| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 712197 | IFAC Proceedings Volumes | 2014 | 6 Pages |
Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. Presently, the linear subsystem may be parametric or not, continuous- or discrete-time. The input nonlinearity is allowed to be a memory operator of backlash type bordered by straight lines. The output nonlinearity may be noninvertible and is only supposed to be welld approximated, within any subinterval belonging to the working interval, with a polynomial of unknown order and parameters. An optimal strategy is presented to identify the system nonlinearities and an identification approach is developed that provides estimates of the linear subsystem. The method involves easily generated excitation signals. Finally, all the suggested estimators are shown consistent.
