  In classical approaches of dynamic network identification, in order to
identify a system (module) embedded in a dynamic network, one has to formulate
a Multi-input-Single-output (MISO) identification problem that requires
identification of a parametric model for all the modules constituting the MISO
setup including (possibly) the noise model, and determine their model order.
This requirement leads to model order selection steps for modules that are of
no interest to the experimenter which increases the computational complexity
for large-sized networks. Also, identification using a parametric noise model
(like BJ method) can suffer from local minima, however neglecting the noise
model has its impact on the variance of the estimates. In this paper, we
provide a two-step identification approach to avoid these problems. The first
step involves performing a non-parametric indirect approach for a MISO
identification problem to get the non-parametric frequency response function
(FRF) estimates and its variance as a function of frequency. In the second
step, the estimated non-parametric FRF of the target module is smoothed using a
parametric frequency domain estimator with the estimated variance from the
previous step as the non-parametric noise model. The developed approach is
practical with weak assumptions on noise, uses the available toolbox, requires
a parametric model only for the target module of interest, and uses a
non-parametric noise model to reduce the variance of the estimates. Numerical
simulations illustrate the potentials of the introduced method in comparison
with the classical identification methods.
