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Robust dynamical network reconstruction
Yuan, Y.; Stan, G. B.; Warnick, S. et al.
2010In The proceedings of the 49th IEEE Conference on Decision and Control (CDC)
Peer reviewed


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Abstract :
[en] Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise-free LTI systems. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). Algorithms are therefore proposed to reconstruct dynamical (Boolean) network structure from time-series (steady-state) data respectively in presence of noise and nonlinearities. In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular Boolean structures. Information criteria and optimisation technique balancing such distance and model complexity are introduced to search for the true structure. We conclude with biologically-inspired network reconstruction examples which include noise and nonlinearities.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Yuan, Y.
Stan, G. B.
Warnick, S.
Goncalves, Jorge ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Language :
Title :
Robust dynamical network reconstruction
Publication date :
Event name :
49th IEEE Conference on Decision and Control (CDC)
Event place :
Atlanta, GA, United States
Event date :
December 15-17, 2010
Main work title :
The proceedings of the 49th IEEE Conference on Decision and Control (CDC)
Publisher :
Pages :
810 - 815
Peer reviewed :
Peer reviewed
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