Program

PROGRAM OF THE 8th SPRING SCHOOL on

Data-Driven Model Learning of Dynamic Systems

Basics of linear system identification 

Lectures on Monday 7 April (afternoon) and on Tuesday 8 April (all day)

Exercises on Wednesday 9 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.

Adaptive parameter estimation - coping with time-varying systems 

Lecture on Wednesday 9 April (beginning of the afternoon)

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

Theme 1: Recursive identification methods for black-box models

Theme 2: Adaptive parameter estimation for gray-box models via extended Kalman filter

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

Lectures on Wednesday 9 April (end of the afternoon) and Thursday 10 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

Dynamic model learning

Lectures on Thursday 10 April (afternoon) and on Friday 11 April (morning)

Lecturer: Håkan Hjalmarsson, Professor, KTH, Stockholm, Sweden

Theme 1: Fundamental parameter estimation concepts: Sufficient statistics, the Cramér-Rao bound, the maximum likelihood estimator, estimator-based methods

Theme 2: Minimum MSE estimators: Bayes estimators, empirical Bayes methods, risk estimation methods, Gaussian processes, asymptotic analysis

Theme 3: Application to dynamical models: linear models, non-linear input-output models, non-linear state-space models

Theme 4: Computational tools: Sampling, Markov Chain Monte Carlo methods, particle filtering and smoothing