Functional observability and target state estimation in large-scale networks.

ABSTRACT: The quantitative understanding and precise control of complex dynamical systems can only be achieved by observing their internal states via measurement and/or estimation. In large-scale dynamical networks, it is often difficult or physically impossible to have enough sensor nodes to make the system fully observable. Even if the system is in principle observable, high dimensionality poses fundamental limits on the computational tractability and performance of a full-state observer. To overcome the curse of dimensionality, we instead require the system to be functionally observable, meaning that a targeted subset of state variables can be reconstructed from the available measurements. Here, we develop a graph-based theory of functional observability, which leads to highly scalable algorithms

SUBMITTER: Montanari AN 

PROVIDER: S-EPMC8740740 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature