57 citations · 132 across the 28 of their papers we have counts for
33 papers
Data-informativity conditions for structured linear systems with implications for dynamic networks
Paul M. J. Van den Hof, Shengling Shi, Stefanie J. M. Fonken +3
When estimating a single subsystem (module) in a linear dynamic network with a prediction error method, a data-informativity condition needs to be satisfied for arriving at a consi…
Balancing Application Relevant and Sparsity Revealing Excitation in Input Design
Javad Parsa, Cristian R. Rojas, Håkan Hjalmarsson
The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation. A regressor matrix whose columns are highly cor…
Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations
Rodrigo A. González, Cristian R. Rojas, Håkan Hjalmarsson
In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods. One common input be…
Learning sparse linear dynamic networks in a hyper-parameter free setting
Arun Venkitaraman, Håkan Hjalmarsson, Bo Wahlberg
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamic…
Robust exploration in linear quadratic reinforcement learning
Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön +1
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex opti…
Hierarchical Robust Analysis for Identified Systems in Network
Anton Korniienko, Xavier Bombois, Hakan Hjalmarsson +1
This technical report considers worst-case robustness analysis of a network of locally controlled uncertain systems with uncertain parameter vectors belonging to the ellipsoid sets…
Weighted Null-Space Fitting for Identification of Cascade Networks
Miguel Galrinho, Riccardo Prota, Mina Ferizbegovic +1
For identification of systems embedded in dynamic networks, applying the prediction error method (PEM) to a correct tailor-made parametrization of the complete network provided asy…
Modeling and identification of uncertain-input systems
Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
In this work, we present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a parti…
Estimating Models with High-Order Noise Dynamics Using Semi-Parametric Weighted Null-Space Fitting
Miguel Galrinho, Cristian R. Rojas, Hakan Hjalmarsson
Standard system identification methods often provide inconsistent estimates with closed-loop data. With the prediction error method (PEM), this issue is solved by using a noise mod…
Parametric Identification Using Weighted Null-Space Fitting
Miguel Galrinho, Cristian R. Rojas, Hakan Hjalmarsson
In identification of dynamical systems, the prediction error method using a quadratic cost function provides asymptotically efficient estimates under Gaussian noise and additional…
An empirical Bayes approach to identification of modules in dynamic networks
Niklas Everitt, Giulio Bottegal, Håkan Hjalmarsson
We present a new method of identifying a specific module in a dynamic network, possibly with feedback loops. Assuming known topology, we express the dynamics by an acyclic network…
Optimal model order reduction with the Steiglitz-McBride method for open-loop data
Niklas Everitt, Miguel Galrinho, Håkan Hjalmarsson
In system identification, it is often difficult to find a physical intuition to choose a noise model structure. The importance of this choice is that, for the prediction error meth…
A new kernel-based approach to system identification with quantized output data
Giulio Bottegal, Håkan Hjalmarsson, Gianluigi Pillonetto
In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covarianc…
Kernel-based system identification from noisy and incomplete input-output data
Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
In this contribution, we propose a kernel-based method for the identification of linear systems from noisy and incomplete input-output datasets. We model the impulse response of th…
ARX modeling of unstable linear systems
Miguel Galrinho, Niklas Everitt, Håkan Hjalmarsson
High-order ARX models can be used to approximate a quite general class of linear systems in a parametric model structure, and well-established methods can then be used to retrieve…
Bayesian kernel-based system identification with quantized output data
Giulio Bottegal, Gianluigi Pillonetto, Håkan Hjalmarsson
In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covarianc…
On the estimation of initial conditions in kernel-based system identification
Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
Recent developments in system identification have brought attention to regularized kernel-based methods, where, adopting the recently introduced stable spline kernel, prior informa…
A new kernel-based approach for overparameterized Hammerstein system identification
Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
In this paper we propose a new identification scheme for Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic syst…
A benchmark for data-based office modeling: challenges related to CO dynamics
Riccardo Sven Risuleo, Marco Molinari, Giulio Bottegal +2
This paper describes a benchmark consisting of a set of synthetic measurements relative to an office environment simulated with the software IDA-ICE. The simulated environment repr…
Variance Analysis of Linear SIMO Models with Spatially Correlated Noise
Niklas Everitt, Giulio Bottegal, Cristian R. Rojas +1
Substantial improvement in accuracy of identified linear time-invariant single-input multi-output (SIMO) dynamical models is possible when the disturbances affecting the output mea…
Blind system identification using kernel-based methods
Giulio Bottegal, Riccardo S. Risuleo, Håkan Hjalmarsson
We propose a new method for blind system identification. Resorting to a Gaussian regression framework, we model the impulse response of the unknown linear system as a realization o…
A kernel-based approach to Hammerstein system identification
Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
In this paper, we propose a novel algorithm for the identification of Hammerstein systems. Adopting a Bayesian approach, we model the impulse response of the unknown linear dynamic…
Robust EM kernel-based methods for linear system identification
Giulio Bottegal, Aleksandr Y. Aravkin, Håkan Hjalmarsson +1
Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic par…
Piecewise Toeplitz Matrices-based Sensing for Rank Minimization
Kezhi Li, Cristian R. Rojas, Saikat Chatterjee +1
This paper proposes a set of piecewise Toeplitz matrices as the linear mapping/sensing operator for recovering l…