Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises

• An identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented.
• A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system.
• The identification of the linear part separated from that of nonlinear part.
• Least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise.

A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises 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 system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.