Additive noise and instantaneous disturbance may produce adverse influences to system identification.However

an output of a real system is often affected by both noise and disturbance.To reduce these disadvantageous influences

system output data polluted by additive noise are repeatedly decomposed

denoised and reconstructed employing different wavelet functions and different threshold methods.After the output data are denoised by wavelet approaches

the identification results obtained by employing the least squares(LS) identification method are still unsatisfactory due to instantaneous disturbances.Hence

An identification algorithm based on wavelet and least absolute errors (WLA) is proposed.The simulation study shows that the presented approach can overcome the influences of noise and disturbance simultaneously.