SC4040 Filtering and identification

Topics: System identification using a least-squares approach
The objective of this course is to show the use of linear algebra and its geometric interpretation in deriving computationally simple and easy to understand solutions to various system theoretical problems. Review of some topics from linear algebra, dynamical system theory and statistics, that are relevant for filtering and system identification. Kalman filtering as a weighted least squares problem. Prediction error and output error system identification as nonlinear least squares problems. Subspace identification based on basic linear algebra tools such as the QR factorization and the SVD. Discussion of some practical aspects in the system identification cycle.


B. Sinquin

Michel Verhaegen

Last modified: 2016-02-25


Credits: 6 EC
Period: 0/4/0/0