Program

PROGRAM OF THE 6th SPRING SCHOOL on

Data-Driven Model Learning of Dynamic Systems

Basics of linear system identification 

Lectures on Monday 3 April (afternoon) and on Tuesday 4 April (afternoon and also possibly in the morning)

Exercises on Wednesday 5 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 and Design of optimal identification experiments 

Lectures on Wednesday 5 April (afternoon)

Exercises on Thursday 6 April (morning)

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

Theme 1: Closed-loop identification: different methods, informativity, ...

Theme 2: Optimal experiment design: formulation as an optimization problem, accuracy and cost constraint

Theme 3: Optimal experiment design: convexification of the optimization problem, parametrization of the to-be-design power spectrum

Theme 4: Optimal experiment design: Alternative formulations, least costly experiment design

Nonlinear system identification

Lectures on Thursday 6 April (afternoon) and on Friday 7 April (morning)

Lecturer: Maarten Schoukens, Assistant Professor, TU Eindhoven, The Netherlands

Theme 1: Best Linear Approximation: Random Phase Multisine, Nonlinearities in the Frequency Domain, Stochastic Nonlinear Distortion, Nonlinearity Quantification and Characterization

Theme 2: Block-Oriented Identification: Model Structures, Structure Detection, Model Initialization, Nonlinearity Decoupling

Theme 3: Nonlinear State-Space Identification: Model Initialization, Stability Issues and Local Minima, Multiple shooting

Theme 4: Artificial Neural Networks: Fundamentals, Encoder Networks, ANN for State-Space Identification