  We present a new method for the identification of linear time-invariant
passive systems from noisy frequency response data. In particular, we propose
to fit a parametrized port-Hamiltonian (pH) system, which is automatically
passive, to supplied data with respect to a least-squares objective function.
In a numerical study, we assess the accuracy of the resulting identified models
by comparing our method to two other frequency domain system identification
methods. One of the methods being compared is a recently published
identification procedure that also computes pH systems and the other one is the
well-known vector-fitting algorithm, which provides unstructured models. The
numerical evaluation demonstrates a substantial increase in accuracy of our
method compared to the other pH identification procedure and a slightly
improved accuracy compared to vector-fitting. This underlines the suitability
of our method for the estimation of passive or pH systems - in particular from
noisy frequency response data.
