Identification of nonlinear continuous-time Hammerstein model using the Fourier modulating function technique

Adaptive weighted least square (AWLS) using Fourier modulating function (FMF) method was applied for the identification of nonlinear continuous-time Hammerstein model. A simulation example is studied. In the example optimal selection of I/O data interval and maximum frequency index of Fourier modulating function have been investigated based on a RMS normalized error criterion. The illustrative simulation studies for the AWLS using FMF show the efficiency of the approach for the parameter identification of a continuous-time Hammerstein system in the presence of significant output measurement disturbances.