187 citations · 532 across the 25 of their papers we have counts for
6 papers · 1 filter
Scenario optimization for optimal training of Echo State Networks
Luca Bugliari Armenio, Lorenzo Fagiano, Enrico Terzi +2
Echo State Networks (ESNs) are widely-used Recurrent Neural Networks. They are dynamical systems including, in state-space form, a nonlinear state equation and a linear output tran…
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification
Fabio Bonassi, Enrico Terzi, Marcello Farina +1
The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a dynamical system perspective. The classical recursive equations describing the evolution o…
Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems
Simone Pozzoli, Marco Gallieri, Riccardo Scattolini
The use of recurrent neural networks to represent the dynamics of unstable systems is difficult due to the need to properly initialize their internal states, which in most of the c…
Hierarchical Control in Islanded DC Microgrids with Flexible Structures
Pulkit Nahata, Alessio La Bella, Riccardo Scattolini +1
Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distrib…
Model predictive control design for dynamical systems learned by Long Short-Term Memory Networks
Enrico Terzi, Fabio Bonassi, Marcello Farina +1
This paper analyzes the stability-related properties of Long Short-Term Memory (LSTM) networks and investigates their use as the model of the plant in the design of Model Predictiv…
Echo State Networks: analysis, training and predictive control
Luca Bugliari Armenio, Enrico Terzi, Marcello Farina +1
The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kin…