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20192022
most citedAllocation of Excitation Signals for Generic Identifiability of Linear Dynamic Networks

24 citations · 50 across the 7 of their papers we have counts for

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eess.SY2022★ 5 cited

Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

H. J. Dreef, S. Shi, X. Cheng +2

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signa…

eess.SY2020★ 1 cited

Single module identifiability in linear dynamic networks with partial excitation and measurement

Shengling Shi, Xiaodong Cheng, Paul M. J. Van den Hof

Identifiability of a single module in a network of transfer functions is determined by whether a particular transfer function in the network can be uniquely distinguished within a…

eess.SY2020★ 18 cited

A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect

Rik J. C. van Esch, Shengling Shi, Antoine Bernas +3

Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addr…

eess.SY2020

Generic identifiability of subnetworks in a linear dynamic network: the full measurement case

Shengling Shi, Xiaodong Cheng, Paul M. J. Van den Hof

Identifiability conditions for single or multiple modules in a dynamic network specify under which conditions the considered modules can be uniquely recovered from the second-order…

eess.SY2019★ 1 cited

Bayesian topology identification of linear dynamic networks

Shengling Shi, Giulio Bottegal, Paul M. J. Van den Hof

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper,…