This tutorial paper is concerned with the identification of hybrid models, ie dynamical models
whose behavior is determined by interacting continuous and discrete dynamics. Methods …

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 …

This paper proposes a three-stage procedure for parametric identification of piecewise affine
autoregressive exogenous (PWARX) models. The first stage simultaneously classifies the …

This paper proposes a general convex framework for the identification of switched linear
systems. The proposed framework uses over-parameterization to avoid solving the …

In this paper we develop a method for learning nonlinear system models with multiple
outputs and inputs. We begin by modeling the errors of a nominal predictor of the system …

This paper deals with the identification of nonlinear systems using piecewise linear models.
By means of a sparse over-parameterization, this challenging problem is turned into a …

In this paper, the problem of clustering based procedure for the identification of PieceWise
Auto-Regressive eXogenous (PWARX) models is addressed. This problem involves both the …

This paper deals with the problem of piecewise auto regressive systems with exogenous
input (PWARX) model identification based on clustering solution. This problem involves both …

Hybrid systems are a class of dynamical systems whose behaviors are based on the
interaction between discrete and continuous dynamical behaviors. Since a general method …

In this paper we develop an algorithm for the identification of piecewise linear output error
models for the case where the discrete mode of the underlying hybrid system is not known …