Abstract
Behind any complex system in nature or engineering, there is an intricate network of interconnections that is often unknown. Using a control-theoretical approach, we study the problem of network reconstruction (NR): inferring both the network structure and the coupling weights based on measurements of each node's activity. We derive two new methods for NR, a low-complexity reduced-order estimator (which projects each node's dynamics to a one-dimensional space) and a full-order estimator for cases where a reduced-order estimator is not applicable. We prove their convergence to the correct network structure using Lyapunov-like theorems and persistency of excitation. Importantly, these estimators apply to systems with partial state measurements, a broad class of node dynamics and internode coupling functions, and in the case of the reduced-order estimator, node dynamics and internode coupling functions that are not fully known. The effectiveness of the estimators is illustrated using both numerical and experimental results on networks of chaotic oscillators.
Original language | English (US) |
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Article number | 083121 |
Journal | Chaos |
Volume | 29 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1 2019 |
Funding
This work was supported by the O ce of Naval Research (ONR) under Grant No. N00014-13-1-0331 and by the Army Research Lab. In addition, we wish to thank the Department of Electrical Engineering and Information Technology, University of Naples Federico II, Italy, and, in particular, to Professor Mario di Bernardo for providing hosting in his lab, and Professor Massimiliano de Magistris and Professor Carlo Petrarca for providing access to their experimental setup. Additionally, we acknowledge the Young Investigator Training Program (YITP)—ISCAS2018 for providing the scholarship that funded the stay of Daniel Alberto Burbano Lombana in Italy. The dataset and codes used in the examples will be provided upon request.
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Mathematical Physics
- General Physics and Astronomy
- Applied Mathematics