57 citations · 132 across the 28 of their papers we have counts for
4 papers · 1 filter
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…