Auxiliary model based recursive extended least squares and maximum likelihood estimation algorithms for input nonlinear systems

This paper studies the identification problems of input nonlinear controlled autoregressive moving average (IN-CARMA) systems, and derived an auxiliary model based recursive extended least squares (AM-RELS) algorithm and a maximum likelihood algorithm based on the Newton optimization method. The simulation results show that the proposed algorithm are effective.