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Network identification


Subject: Network identification
Staff Mentor: X.J.A. Bombois
Other Mentor(s): Arne Dankers, Alessandro Abate, Peter Heuberger and Paul Van den Hof
Keywords: System identification and estimation; Fundamentals; Distributed control
Description: Data-based modeling (system identification) is the scientific exercise consisting of determining a mathematical model of a system using input-output data collected on that system. Identifying a model of the system can be done using well-established techniques when the data are collected in open loop or in a classical closed-loop setting.

However, we observe that the systems are nowadays becoming increasingly complex and interconnected. These interconnections are much more complex than a classical closed loop. Consequently, the classical closed-loop techniques cannot be directly applied to identify such interconnected systems.

In this project, the objective is to extend the closed-loop identification techniques to be able

  • to determine the interconnection structure in a complex network
  • and to identify the dynamics between the different signals in such a network.

The identified models of such complex interconnected systems could be then used to control those networks using distributed control.

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Last modified: 12 April 2012, 15:28 UTC
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