[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 :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
Yuan, Y.
Stan, G. B.
Warnick, S.
GONCALVES, Jorge ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Langue du document :
Anglais
Titre :
Robust dynamical network reconstruction
Date de publication/diffusion :
2010
Nom de la manifestation :
49th IEEE Conference on Decision and Control (CDC)
Lieu de la manifestation :
Atlanta, GA, Etats-Unis
Date de la manifestation :
December 15-17, 2010
Titre de l'ouvrage principal :
The proceedings of the 49th IEEE Conference on Decision and Control (CDC)
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