Dynamic network structure identification with prediction error methods - basic examples

Abstract Modeling of dynamical properties of highly complex and interconnected systems becomes important in different fields of science. When identifying the structure and dynamics of a network of interconnected dynamical systems, including cause-effect relations, there is a tendency to use nonparametric or FIR models of the output error type. In this paper it is shown, and illustrated by some simple examples, that appropriate attention should be given to using flexible noise models, in order to allow consistent identification of the dynamics, while the use of external excitation/probing signals may reduce this need. It is a first step towards using prediction error identification tools to identify the structure of a network.