Learning local modules in dynamic networks without prior topology information

Recently different identification methods have been developed for identifying a single module in a dynamic network. In order to select an appropriate predictor model one typically needs prior knowledge on the topology (interconnection structure) of the dynamic network, as well as on the correlation structure of the process disturbances. In this paper we present a new approach that incorporates the estimation of this prior information into the identification, leading to a fully data-driven approach for estimating the dynamics of a local module. The developed algorithm uses non-causal Wiener filters and a series of convex optimizations with parallel computation capabilities to estimate the topology, which subsequently is used to build the appropriate input/output setting for a predictor model in the local direct method under correlated process noise. A regularized kernel-based method is then employed to estimate the dynamic of the target module. This leads to an identification algorithm with attractive statistical properties that is scalable to handle larger-scale networks too. Numerical simulations illustrate the potential of the developed algorithm.