48 citations · 117 across the 22 of their papers we have counts for
24 papers
Deep networks for system identification: a Survey
Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon +3
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results i…
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…
Tutorial on Asymptotic Properties of Regularized Least Squares Estimator for Finite Impulse Response Model
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we give a tutorial on asymptotic properties of the Least Square (LS) and Regularized Least Squares (RLS) estimators for the finite impulse response model with filter…
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Regularized Least Squares Regression Problems
Biqiang Mu, Tianshi Chen, Lennart Ljung
The asymptotic optimality (a.o.) of various hyper-parameter estimators with different optimality criteria has been studied in the literature for regularized least squares regressio…
A Crash Course on Reinforcement Learning
Farnaz Adib Yaghmaie, Lennart Ljung
The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introductio…
Supplementary Material for CDC Submission No. 1461
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we focus on the influences of the condition number of the regression matrix upon the comparison between two hyper-parameter estimation methods: the empirical Bayes (…
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.…
Algorithms and Performance Analysis for Stochastic Wiener System Identification
Bo Wahlberg, Lennart Ljung
We analyze the statistical performance of identification of stochastic dynamical systems with non-linear measurement sensors. This includes stochastic Wiener systems, with linear d…
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…
Generalized Kalman Smoothing: Modeling and Algorithms
A. Y. Aravkin, J. V. Burke, L. Ljung +2
State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions,…
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…
Identification of Stochastic Wiener Systems using Indirect Inference
Bo Wahlberg, James Welsh, Lennart Ljung
We study identification of stochastic Wiener dynamic systems using so-called indirect inference. The main idea is to first fit an auxiliary model to the observed data and then in a…
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…
Maximum Entropy Kernels for System Identification
Francesca Paola Carli, Tianshi Chen, Lennart Ljung
A new nonparametric approach for system identification has been recently proposed where the impulse response is modeled as the realization of a zero-mean Gaussian process whose cov…
Scalable Anomaly Detection in Large Homogenous Populations
Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad +2
Anomaly detection in large populations is a challenging but highly relevant problem. The problem is essentially a multi-hypothesis problem, with a hypothesis for every division of…