Abstract
In the modelling of dynamic systems, increasing accuracy requirements and the usage of software tools lead to models of high order. These models can significantly be simplified by model reduction. Krylov Subspace Methods allow reducing even very high order models with several ten thousands of state variables. This paper gives an introduction into the basic concepts, presents the most important algorithms, and gives a short outlook into open questions.
| Translated title of the contribution | Order reduction using krylov subspace methods |
|---|---|
| Original language | German |
| Pages (from-to) | 30-38 |
| Number of pages | 9 |
| Journal | At-Automatisierungstechnik |
| Volume | 52 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2004 |
| Externally published | Yes |