16 citations · 71 across the 18 of their papers we have counts for
22 papers
Learning linear modules in a dynamic network with missing node observations
Karthik R. Ramaswamy, Giulio Bottegal, Paul M. J. Van den Hof
In order to identify a system (module) embedded in a dynamic network, one has to formulate a multiple-input estimation problem that necessitates certain nodes to be measured and in…
Learning linear modules in a dynamic network using regularized kernel-based methods
Karthik R. Ramaswamy, Giulio Bottegal, Paul M. J. Van den Hof
In order to identify one system (module) in an interconnected dynamic network, one typically has to solve a Multi-Input-Single-Output (MISO) identification problem that requires id…
Bayesian topology identification of linear dynamic networks
Shengling Shi, Giulio Bottegal, Paul M. J. Van den Hof
In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper,…
Local module identification in dynamic networks with correlated noise: the full input case
Paul M. J. Van den Hof, Karthik R. Ramaswamy, Arne G. Dankers +1
The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module…
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…
The Generalized Cross Validation Filter
Giulio Bottegal, Gianluigi Pillonetto
Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques. A notable e…
Preserving Privacy of Finite Impulse Response Systems
Giulio Bottegal, Farhad Farokhi, Iman Shames
Adding input and output noises for increasing model identification error of finite impulse response (FIR) systems is considered. This is motivated by the desire to protect the mode…
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…
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…
Boosting as a kernel-based method
Aleksandr Y. Aravkin, Giulio Bottegal, Gianluigi Pillonetto
Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-…
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…
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…
Outlier robust system identification: a Bayesian kernel-based approach
Giulio Bottegal, Aleksandr Y. Aravkin, Hakan Hjalmarsson +1
In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian proces…
On the Zero-freeness of Tall Multirate Linear Systems
Mohsen Zamani, Giulio Bottegal, Brian D. O. Anderson
In this paper, tall discrete-time linear systems with multirate outputs are studied. In particular, we focus on their zeros. In systems and control literature zeros of multirate sy…
Modeling complex systems by Generalized Factor Analysis
Giulio Bottegal, Giorgio Picci
We propose a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of…