Identification of fractional order Hammerstein models based on mixed signals

An algorithm based on mixed signals is proposed, to solve the issues of low accuracy of identification algorithm, immeasurable intermediate variables of fractional order Hammerstein model, and how to determine the magnitude of fractional order. In this paper, a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently. The nonlinear part is fitted by the neural fuzzy network model, which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models. In addition, the multi-innovation Levenberg-Marquardt (MILM) algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results. A simulation example is given to verify the accuracy and effectiveness of the proposed method.