  In this paper, the problem of choosing the best allocation of excitations and
measurements for the identification of a dynamic network is formally stated and
analyzed. The best choice will be one that achieves the most accurate
identification with the least costly experiment. Accuracy is assessed by the
trace of the asymptotic covariance matrix of the parameters estimates, whereas
the cost criterion is the number of excitations and measurements. Analytical
and numerical results are presented for two classes of dynamic networks in
state space form: branches and cycles. From these results, a number of
guidelines for the choice emerge, which are based either on the topology of the
network or on the relative magnitude of the modules being identified. An
example is given to illustrate that these guidelines can to some extent be
applied to networks of more generic topology.
