Auxiliary model based recursive generalized least squares parameter estimation for Hammerstein OEAR systems

This paper deals with the parameter identification problem of Hammerstein output error auto-regressive (OEAR) systems with different nonlinearities by combining the key-term separation principle and the auxiliary model identification idea. The basic idea is, by using the key-term separation principle, to present auxiliary model based recursive generalized least squares algorithms in terms of the auxiliary model idea. The proposed algorithm can obtain the system model parameter estimates and the noise model parameter estimates, and can be extended to other nonlinear systems.