Parameter estimation of Hammerstein model based on wavelet packet

For the nonlinear Hammerstein model with the noise corrupted output data, a method is proposed to estimate the parameters of the model with the input-output data by wavelet packet decomposition. The nonlinear static memoryless part of Hammerstein model can be represented as a polynomial of the input, the following linear part is an ARMAX model. The parameters of model are estimated by the least squared (LS) method in wavelet domain, compare with the least squared method in time domain, the proposed method is feasible and effective by the simulation.