Combinatorial Characterization for Global Identifiability of Separable Networks with Partial Excitation and Measurement
(2024) 2023 62nd IEEE Conference on Decision and Control (CDC) — Location: Singapore, Singapore (13.December.2023)
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- Abstract
- This work focuses on the generic identifiability of dynamical networks with partial excitation and measurement: a set of nodes are interconnected by transfer functions according to a known topology, some nodes are excited, some are measured, and only a part of the transfer functions are known. Our goal is to determine whether the unknown transfer functions can be generically recovered based on the input-output data collected from the excited and measured nodes. We introduce the notion of separable networks, for which global and so-called local identifiability are equivalent. A novel approach yields a necessary and sufficient combinatorial characterization for local identifiability for such graphs, in terms of existence of paths and conditions on their parity. Furthermore, this yields a necessary condition not only for separable networks, but for networks of any topology.
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Legat, A., & Hendrickx, J. (2024). Combinatorial Characterization for Global Identifiability of Separable Networks with Partial Excitation and Measurement. 2023 62nd IEEE Conference on Decision and Control (CDC), p. 2471-2476. https://doi.org/10.1109/cdc49753.2023.10383708