We propose a methodology for the identification of nonlinear state–space models from
input/output data using machine-learning techniques based on autoencoders and neural …

Abstract Using Artificial Neural Networks (ANN) for nonlinear system identification has
proven to be a promising approach, but despite of all recent research efforts, many practical …

Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

Learning-based control approaches have shown great promise in performing complex tasks
directly from high-dimensional perception data for real robotic systems. Nonetheless, the …

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for
system identification. Although DNNs show impressive approximation ability in various …

The present paper treats the identification of nonlinear dynamical systems using Koopman-
based deep state-space encoders. Through this method, the usual drawback of needing to …

The Linear Parameter-Varying (LPV) framework provides a modeling and control design
toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite …

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 …

The challenge for next generation system identification is to build new flexible models and
estimators able to simulate complex systems. This task is especially difficult in the nonlinear …

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 …