Non-parametric identification in dynamic networks

In this paper we present a non-parametric approach to identification in networks. The main advantage of a non-parametric approach is that consistent estimates can be obtained with very little prior knowledge about the system. This is a particularly important consideration for a network identification problem which can easily become very complex with high order dynamics and many inputs. We consider a very general framework for dynamic networks that includes measured variables, external excitation variables, process noise, and sensor noise.