Data-driven approximation and reduction from noisy data in matrix pencil frameworks

02/19/2022
by   Pauline Kergus, et al.
0

This work aims at tackling the problem of learning surrogate models from noisy time-domain data by means of matrix pencil-based techniques, namely the Hankel and Loewner frameworks. A data-driven approach to obtain reduced order state-space models from time-domain input-output measurements for linear time-invariant (LTI) systems is proposed. This is accomplished by combining the aforementioned model order reduction (MOR) techniques with the signal matrix model (SMM) approach. The proposed method is illustrated by a numerical benchmark example consisting of a building model.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset