24 citations · 50 across the 7 of their papers we have counts for
8 papers
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…
Identifiability in Dynamic Acyclic Networks with Partial Excitations and Measurements
Xiaodong Cheng, Shengling Shi, Ioannis Lestas +1
This paper deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considerin…
A Necessary Condition for Network Identifiability with Partial Excitation and Measurement
Xiaodong Cheng, Shengling Shi, Ioannis Lestas +1
This paper considers dynamic networks where vertices and edges represent manifest signals and causal dependencies among the signals, respectively. We address the problem of how to…
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…
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…
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…
Allocation of Excitation Signals for Generic Identifiability of Linear Dynamic Networks
Xiaodong Cheng, Shengling Shi, Paul M. J. Van den Hof
A recent research direction in data-driven modeling is the identification of dynamic networks, in which measured vertex signals are interconnected by dynamic edges represented by c…
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,…