140 citations · 235 across the 9 of their papers we have counts for
10 papers
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification
Marco Forgione, Dario Piga
Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-c…
Learning neural state-space models: do we need a state estimator?
Marco Forgione, Manas Mejari, Dario Piga
In recent years, several algorithms for system identification with neural state-space models have been introduced. Most of the proposed approaches are aimed at reducing the computa…
NARX Identification using Derivative-Based Regularized Neural Networks
L. H. Peeters, G. I. Beintema, M. Forgione +1
This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential deca…
On the adaptation of recurrent neural networks for system identification
Marco Forgione, Aneri Muni, Dario Piga +1
This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems. A nominal RNN model is f…
Deep learning with transfer functions: new applications in system identification
Dario Piga, Marco Forgione, Manas Mejari
This paper presents a linear dynamical operator described in terms of a rational transfer function, endowed with a well-defined and efficient back-propagation behavior for automati…
Continuous-time system identification with neural networks: Model structures and fitting criteria
Marco Forgione, Dario Piga
This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the s…
dynoNet: a neural network architecture for learning dynamical systems
Marco Forgione, Dario Piga
This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dyn…
Model structures and fitting criteria for system identification with neural networks
Marco Forgione, Dario Piga
This paper focuses on the identification of dynamical systems with tailor-made model structures, where neural networks are used to approximate uncertain components and domain knowl…
Efficient Calibration of Embedded MPC
Marco Forgione, Dario Piga, Alberto Bemporad
Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints. A challe…
Performance-oriented model learning for data-driven MPC design
Dario Piga, Marco Forgione, Simone Formentin +1
Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. Howev…