Nonlinear identification in the presence of correlated noise using a Hammerstein model

A simple iterative technique for the estimation of parameters in a Hammerstein model is developed for the case when noise in the output data is correlated. Asymptotically unbiased estimates are obtained rapidly by employing a systematic procedure for the determination of a scalar stepping factor at each iteration.