Data filtering based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition
نویسندگان
چکیده
This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition based least squares iterative algorithm is presented for estimating the parameter vectors in each subsystem. Moreover, a data filtering based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.
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