  This paper deals with dynamic networks in which the causality relations
between the vertex signals are represented by linear time-invariant transfer
functions (modules). Considering an acyclic network where only a subset of its
vertices are measured and a subset of the vertices are excited, we explore
conditions under which all the modules are identifiable on the basis of
measurement data. Two sufficient conditions are presented in the paper, where
the first condition concerns an identifiability analysis that needs to be
performed for each vertex, while the second condition, based on the concept of
tree/anti-tree covering, results from a graphical synthesis approach to
allocate actuators and sensors in an acyclic network for achieving generic
identifiability.
