5 citations · 14 across the 7 of their papers we have counts for
7 papers
A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systems
Zhexuan Zeng, Zuogong Yue, Alexandre Mauroy +2
Low sampling frequency challenges the exact identification of the continuous-time (CT) dynamical system from sampled data, even when its model is identifiable. The necessary and su…
DeceFL: A Principled Decentralized Federated Learning Framework
Ye Yuan, Jun Liu, Dou Jin +12
Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherent…
A state-space approach to sparse dynamic network reconstruction
Zuogong Yue, Johan Thunberg, Lennart Ljung +1
Dynamic network reconstruction has been shown to be challenging due to the requirements on sparse network structures and network identifiability. The direct parametric method (e.g.…
Dynamic Network Reconstruction from Heterogeneous Datasets
Zuogong Yue, Johan Thunberg, Wei Pan +2
Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations to parameters or external disturbances.…
Inverse Problems for Matrix Exponential in System Identification: System Aliasing
Zuogon Yue, Johan Thunberg, Jorge Goncalves
This note addresses identification of the -matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse st…
Identification of Sparse Continuous-Time Linear Systems with Low Sampling Rate: Optimization Approaches
Zuogong Yue, Johan Thunberg, Lennart Ljung +1
This paper addresses identification of sparse linear and noise-driven continuous-time state-space systems, i.e., the right-hand sides in the dynamical equations depend only on a su…
Systems Aliasing in Dynamic Network Reconstruction: Issues on Low Sampling Frequencies
Zuogon Yue, Johan Thunberg, Lennart Ljung +2
Network reconstruction of dynamical continuous-time (CT) systems is motivated by applications in many fields. Due to experimental limitations, especially in biology, data could be…