Identifiability of dynamical networks: which nodes need be measured?

TL;DR

This paper investigates which nodes in a known-topology dynamical network need to be measured for identifiability, showing that often only a small subset of measurements suffices under certain assumptions.

Contribution

It provides the first results for network identifiability with partial measurements, using graph theory to determine which nodes must be measured.

Findings

Network can often be identified with few node measurements

Identifiability depends on the topology of paths to measured nodes

Results apply when nodes are noise-free and each has known excitation

Abstract

Much recent research has dealt with the identifiability of a dynamical network in which the node signals are connected by causal linear time-invariant transfer functions and are possibly excited by known external excitation signals and/or unknown noise signals. So far all results on the identifiability of the whole network have assumed that all node signals are measured. Under this assumption, it has been shown that such networks are identifiable only if some prior knowledge is available about the structure of the network, in particular the structure of the excitation. In this paper we present the first results for the situation where not all node signals are measurable, under the assumptions that the topology of the network is known, that each node is excited by a known signal and that the nodes are noise-free. Using graph theoretical properties, we show that the transfer functions that can be identified depend essentially on the topology of the paths linking the corresponding vertices to the measured nodes. An important outcome of our research is that, under those assumptions, a network can often be identified using only a small subset of node measurements.