The paper presents a methodology for identifying the topology of strictly causal dynamical networks. Using a graph-theoretic representation of interconnected dynamical systems, each node is assumed to be a stochastic process whose output is influenced by an independent stochastic noise and outputs of other nodes. The links, which represent the influence of other nodes, are assumed to be strictly causal dynamical filters. When an invasive number of measured data is available, reconstruction techniques based on Granger causality are capable of tackling this problem, however, for very large networks they are rendered impractical. Under the assumption of the sparsity of a complex network including feedback and self-loops, we propose a consistent algorithm based on cycling least-squares that unveils the topology without any a priori knowledge about the structure. For non-invasive measurements, we cast the problem as a structured sparse signal recovery and propose another data-driven algorithm based on ideas borrowed from compressive sensing and matching pursuit to identify the network. To show the superiority of this method we compare its performance with those of the Granger causality and other state-of-the-art techniques through Monte-Carlo simulations.
|Original language||English (US)|
|Title of host publication||2018 IEEE Conference on Decision and Control, CDC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jul 2 2018|
|Event||57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States|
Duration: Dec 17 2018 → Dec 19 2018
|Name||Proceedings of the IEEE Conference on Decision and Control|
|Conference||57th IEEE Conference on Decision and Control, CDC 2018|
|Period||12/17/18 → 12/19/18|
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© 2018 IEEE.