Program

PROGRAM OF THE 9th SPRING SCHOOL on

Data-Driven Model Learning of Dynamic Systems

Basics of linear system identification 

Lectures on Monday 20 April (afternoon) and on Tuesday 21 April (all day)

Exercises on Wednesday 22 April (morning)

Lecturer: Xavier Bombois, CNRS Research Director, Laboratoire Ampère, Ecole Centrale de Lyon

Theme 1: Introduction;concepts; identification cycle

Theme 2: Parametric (prediction error) identification methods: prediction criterion and model structures, linear and pseudo-linear regressions, conditions on data, statistical and asymptotic properties, model set selection and model validation

Theme 3: Non-parametric identification (ETFE)

Theme 4: Experiment design.

Closed-loop identification 

Lecture on Wednesday 22 April (beginning of the afternoon)

Lecturer: Xavier Bombois, CNRS Research Director, Laboratoire Ampère, Ecole Centrale de Lyon

Theme 1: Direct closed-loop method

Theme 2: Indirect closed-loop methods

Gray-box and black-box state-space model learning

Lectures on Wednesday 22 April (end of the afternoon) and Thursday 23 April (morning)

Lecturer: Guillaume Mercère, Associate Professor, Laboratoire LIAS, Université de Poitiers

Theme 1: Black-box model learning: subspace state-space model identification

Theme 2: Gray-box model learning: nonlinear least-squares state-space model identification

Theme 3: From black-box to gray-box models

Deep Learning for System Identification

Lectures on Thursday 23 April (afternoon) and on Friday 24 April (morning)

Lecturer: Marco Forgione, Senior Researcher, IDSIA, Lugano, Switzerland

Theme 1: Introduction to Deep Learning

Theme 2: Feedforward neural networks for static regression

Theme 3: Recurrent neural networks for dynamical systems modeling