48 citations · 117 across the 22 of their papers we have counts for
14 papers · 1 filter
Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator
Yue Ju, Biqiang Mu, Lennart Ljung +1
Regularized system identification is the major advance in system identification in the last decade. Although many promising results have been achieved, it is far from complete and…
Improving Linear State-Space Models with Additional Iterations
Suat Gumussoy, Ahmet Arda Ozdemir, Tomas McKelvey +3
An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation…
Deep State Space Models for Nonlinear System Identification
Daniel Gedon, Niklas Wahlström, Thomas B. Schön +1
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. Th…
Nonlinear System Identification: A User-Oriented Roadmap
Johan Schoukens, Lennart Ljung
The goal of this article is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound s…
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.…
On Asymptotic Properties of Hyperparameter Estimators for Kernel-based Regularization Methods
Biqiang Mu, Tianshi Chen, Lennart Ljung
The kernel-based regularization method has two core issues: kernel design and hyperparameter estimation. In this paper, we focus on the second issue and study the properties of sev…
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.…
Gray Box Identification of State-Space Models Using Difference of Convex Programming
Chengpu Yu, Lennart Ljung, Michel Verhaegen
Gray-box identification is prevalent in modeling physical and networked systems. However, due to the non-convex nature of the gray-box identification problem, good initial paramete…
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…
Identifying Biochemical Reaction Networks From Heterogeneous Datasets
Wei Pan, Ye Yuan, Lennart Ljung +2
In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain…
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso +2
Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the ad…
Maximum entropy properties of discrete-time first-order stable spline kernel
Tianshi Chen, Tohid Ardeshiri, Francesca P. Carli +3
The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a ze…
Regularized system identification using orthonormal basis functions
Tianshi Chen, Lennart Ljung
Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal ba…