The identification of neuro-fuzzy based MIMO Hammerstein model with separable input signals

A novel identification method of neuro-fuzzy based MIMO Hammerstein model by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein process, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, the identification of the dynamic linear element can be separated from the static nonlinear element without any redundant adjustable parameters. Moreover, it can circumvent the problem of initialization and convergence of the model parameters discussed in the existing iterative algorithms used for identification of MIMO Hammerstein model. Examples are used to illustrate the effectiveness of the proposed method.