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Blind identification of sparse dynamic networks and applications

Ayazoglu, Mustafa and Sznaier, Mario and Ozay, Necmiye (2011) Blind identification of sparse dynamic networks and applications. In: IEEE Conference on Decision and Control and European Control Conference. IEEE , Piscataway, NJ, pp. 2944-2950. ISBN 978-1-61284-801-3.

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This paper considers the problem of identifying the topology of a sparsely interconnected network of dynamical systems from experimental noisy data. Specifically, we assume that the observed data was generated by an underlying, unknown graph topology where each node corresponds to a given time-series and each link to an unknown autoregressive model that maps those time series. The goal is to recover the sparsest (in the sense of having the fewest number of links) structure compatible with some a-priori information and capable of explaining the observed data. Contrary to related existing work, our framework allows for (unmeasurable) exogenous inputs, intended to model relatively infrequent events such as environmental or set-point changes in the underlying processes. The main result of the paper shows that both the network topology and the unknown inputs can be identified by solving a convex optimization problem, obtained by combining Group-Lasso type arguments with a re-weighted heuristics. As shown here, this combination leads to substantially sparser topologies than using either group Lasso or orthogonal decomposition based algorithms. These results are illustrated using both academic examples and several non-trivial problems drawn from multiple application domains that include finances, biology and computer vision.

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Additional Information:© 2011 IEEE. This work was supported in part by NSF grants IIS–0713003 and ECCS-0901433, AFOSR grant FA9550-09-1-0253 and the Alert DHS Center of Excellence under Award Number 2008-ST-061-ED0001. The authors are indebted to Professor Uri Alon, Weizmann Institute, and Dr. Alon Zaslaver, Caltech, for providing the diauxic shift experimental data used in Example 6. The research that generated this data was supported by the Kahn Family Foundation at the Weizmann Institute of Science.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-09-1-0253
Alert DHS Center of Excellence2008-ST-061-ED0001
Kahn Family FoundationUNSPECIFIED
Weizmann Institute of ScienceUNSPECIFIED
Record Number:CaltechAUTHORS:20170306-165755539
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Official Citation:M. Ayazoglu, M. Sznaier and N. Ozay, "Blind identification of sparse dynamic networks and applications," 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, 2011, pp. 2944-2950. doi: 10.1109/CDC.2011.6161088
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74819
Deposited By: Kristin Buxton
Deposited On:07 Mar 2017 16:05
Last Modified:03 Oct 2019 16:43

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