After successful completion of the course, students are able to achieve two targets of data-driven modelling:

1. For a given application problem with existing measurement data both the selection of a suitable model structure as well as the optimal estimation of the optimal model parameters can be performed. Alternative models can be assessed in a quantitative way and can be validated by state-of-the-art methods.
2. Advanced literature on methods for system identification can be acquired and implemented idependently.

Contents of the lecture are topics of practical exercises, and the application of dedicated software tools is execised.

Presentation using white-board and beamer, presentation of computational examples using MATLAB/Simulink, links to current research projects at the department, discussion of results and alternatives.

The exercise is actually integrated in the respective lecture comprising presentations of computational examples and homeworks.

The exercises will be held in parallel to the lecture as sson as sufficient contents have been presented.

The course will be held at the computer lab. A login-account is mandatory for all participants and can be created during the course.

Several homeworks similar to the examples done in the lecture are handed out. They have to be completed by the student and handed in at the end of the course.

Stochstics and Fundamentals of Feedback Control are compulsory. Especially Digital Control is nice to have although not compulsory.