Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems

We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input–output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.