Order identification of transfer function-noise model

Classical methods for estimating transfer function models have not always been successful. A statistic approach to the identification of transfer function models which is corrupted by disturbances or noise is presented. The estimated impulse response is obtained from the autocorrelation function and cross correlation function between the measured input and output. Several data analysis tools such as R- , S- and GPAC array for the estimated impulse response give us pretty clear information on the order of transfer function models.