EE-607 / 4 credits

Teacher(s): Paolone Mario, Frigo Guglielmo

Language: English

Remark: Next time: Fall 2021


Frequency

Every year

Summary

This course introduces the principles of model identification for non-linear dynamic systems, and provides a set of possible solution methods that are thoroughly characterized in terms of modelling assumptions and uncertainty levels.

Content

Keywords

Model identification, non-linear system.

Learning Prerequisites

Required courses

Basics in linear algebra, probability, automatics.

Assessment methods

Project report.

In the programs

  • Number of places: 20
  • Exam form: Project report (session free)
  • Subject examined: Advanced Methods for Model Identification
  • Lecture: 34 Hour(s)
  • Practical work: 8 Hour(s)
  • Number of places: 20
  • Exam form: Project report (session free)
  • Subject examined: Advanced Methods for Model Identification
  • Lecture: 34 Hour(s)
  • Practical work: 8 Hour(s)

Reference week