Algorithms developed in the area of graphical models have proven to be useful tools for the analysis and reconstruction of networks of dynamic systems with directed acyclic structure. However, such techniques cannot typically provide a consistent reconstruction for networks of dynamic systems in presence of feedback mechanisms. On the other hand, reconstruction techniques based on causal estimators and one-step-ahead predictors, such as Granger causality, are capable of reconstructing loopy networks, but only if all the operators defining the input/output structure of the network are strictly causal. In this work, we develop a novel reconstruction method for network topologies that combines the advantages of graphical models and causal estimator techniques unifying both approaches under a single framework. The fundamental result is a generalization of Granger causality which provides a consistent reconstruction of the topology of a network of linear dynamic systems under the milder assumption that every loop contains at least one strictly causal transfer function.
|Original language||English (US)|
|Title of host publication||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jan 18 2018|
|Event||56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia|
Duration: Dec 12 2017 → Dec 15 2017
|Name||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017|
|Other||56th IEEE Annual Conference on Decision and Control, CDC 2017|
|Period||12/12/17 → 12/15/17|
Bibliographical noteFunding Information:
*This work is partially supported by NSF (CISE/CAREER:1553504).
© 2017 IEEE.