Correlation analysis-based error compensation recursive least-square identification method for the Hammerstein model

In this paper, the correlation analysis based error compensation recursive least-square (RLS) identification method is proposed for the Hammerstein model. Firstly, the covariance matrix between input and output data points of the Hammerstein model is derived by using separable signal to realize that the unmeasurable internal variable is replaced by the covariance matrix of input. Thus, the correlation analysis method can be accordingly used to estimate parameters of the linear part, which results in the identification problem of the nonlinear part separated from the linear part. In addition, a correction term is added to least-square estimation to compensate error caused by output noise, further the error compensation-based RLS method is obtained for the observed data from the Hammerstein model. As a result, the least-square identification method, which generates error in the presence of noise distribution, can be compensated. Finally, simulation experiments are conducted to illustrate the performance of the proposed identification method.