TY - JOUR
T1 - Optimal Selection of Observations for Identification of Multiple Modules in Dynamic Networks
AU - Jahandari, Sina
AU - Materassi, Donatello
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - This article presents a systematic algorithm to select a set of auxiliary measurements in order to consistently identify certain transfer functions in a dynamic network from observational data. The selection of the auxiliary measurements is obtained by minimizing an appropriate cost function. It is assumed that the topology of the network is known, the forcing inputs are not measured, and the observations have positive additive costs. It is shown that sufficient and necessary conditions for consistent identification of a single transfer function based on a multi-input single-output prediction error method, are equivalent to the notion of minimum cut in an augmented graph resulted from systematically manipulating the graphical representation of the network. Then, the optimal set of auxiliary measurements minimizing the cost could be found using different approaches such as algorithms from graph theory (i.e., Ford-Fulkerson), distributed algorithms (i.e., push-relabel algorithm), or purely optimization based procedures (i.e., linear programming). The results are also extended to the more challenging scenario, where the objective is simultaneously identifying multiple transfer functions.
AB - This article presents a systematic algorithm to select a set of auxiliary measurements in order to consistently identify certain transfer functions in a dynamic network from observational data. The selection of the auxiliary measurements is obtained by minimizing an appropriate cost function. It is assumed that the topology of the network is known, the forcing inputs are not measured, and the observations have positive additive costs. It is shown that sufficient and necessary conditions for consistent identification of a single transfer function based on a multi-input single-output prediction error method, are equivalent to the notion of minimum cut in an augmented graph resulted from systematically manipulating the graphical representation of the network. Then, the optimal set of auxiliary measurements minimizing the cost could be found using different approaches such as algorithms from graph theory (i.e., Ford-Fulkerson), distributed algorithms (i.e., push-relabel algorithm), or purely optimization based procedures (i.e., linear programming). The results are also extended to the more challenging scenario, where the objective is simultaneously identifying multiple transfer functions.
KW - Dynamic networks
KW - identification
KW - optimal observations
UR - http://www.scopus.com/inward/record.url?scp=85131754709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131754709&partnerID=8YFLogxK
U2 - 10.1109/TAC.2022.3179213
DO - 10.1109/TAC.2022.3179213
M3 - Article
AN - SCOPUS:85131754709
SN - 0018-9286
VL - 67
SP - 4703
EP - 4716
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 9
ER -