Program of the 2nd Spring Doctoral School on
Data-driven Model Identification of Dynamical Systems
Polytech Nancy, France
March, 26-29, 2018
The 4-days Spring Doctoral School will consist of a series of lectures followed by computer exercises.
For the computer exercices, it is required that participants bring their own laptop with the latest version of Matlab installed with stand alone license.
March 26, 2018 - 8h30-9h00
Registration - Hall of Building A - Polytech Nancy
March 26, 2018 - 9h00-17h00
Lecturer: Prof. Johan Schoukens (Vrije Universiteit Universiteit, Belgium)
One-day introductory course on System Identification
March 27-28, 2018 - 8h45-17h00
Lecturer: DR. Xavier Bombois (CNRS, Ecole centrale de Lyon, France)
A two-day course on data-driven approaches for discrete-time model identification
- Theme 1: Introduction;concepts; discrete-time signal and system analysis.
- Theme 2: Parametric (prediction error) identification methods: prediction criterion and model structures, linear and pseudo-linear regressions, conditions on data, statistical and asymptotic properties.
- Exercise: Hands on identification of discrete-time parametric models.
- Theme 3: Parametric (prediction error) identification methods: model set selection and model validation, approximate modelling.
- Theme 4: Non-parametric identification (ETFE); experiment design.
- Computer exercise : Getting hands on experience with the Matlab System Identification toolbox.
March 29, 2018 - 8h45-12h15
Lecturer: Prof. Hugues Garnier (University of Lorraine, France)
An overview of data-driven approaches for direct continuous-time model identification
- Theme: Overview of continuous-time linear model identification. Basic SVF-based and optimal instrumental variable (IV)-based estimators. Benefits for practical applications. Software aspects.
- Computer exercise. Getting hands on experience with the CONTSID toolbox methods with real data.
March 29, 2018 - 13h45-16h00
Lecturer: Mathieu Cuenant (MathWorks)
Identification and control - An industrial perspective