Parameter estimation of hammerstein model based on a recursive algorithm in wavelet domain

For the discrete 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 in wavelet domain directly. The recursive least squared (RLS) method is an online method for parameter estimation. With the wavelet theory being developed it plays an important role in signal processing. By means of wavelet transform, the signal has both characteristics of time and frequency and becomes a signal in wavelet domain, and increasing the ratio of signal to noise, the denoising result is more effective than in time domain and in frequency domain. The parameters of model are estimated by the wavelet RLS method, compare with the RLS method in time domain, the proposed method is effective by the simulation.