Based on approximate least absolute deviation and principal component analysis

the partial approximate least absolute deviation for non-linear system identification is carried out aiming at multivariable Hammerstein model with linear correlation of input signals. An approximate least absolute deviation objective function is established by introducing a deterministic function to replace the absolute value under certain situations. The proposed method can overcome the disadvantage of large square residual of least square criterion when the identification data is disturbed by the impulse noise which obeys symmetrical alpha stable (SαS) distribution. By adopting principal component analysis to eliminate the linear correlation among the elements of data vector of nonlinear systems

the unique solution of model parameters can be easily acquired by the proposed method. The simulation shows that the proposed method has stronger robustness than the partial least square (PLS) method in the identification of multivariable Hammerstein model with white noise and impulse noise under the above situation.