.png){kind=icon}
.png){kind=icon}
Return
Interpolatory weighted-H2 model reduction
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2013.01.040.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces an interpolation framework for the weighted-H-2 model reduction problem. We obtain a new representation of the weighted-H-2 norm of SISO systems that provides new interpolatory first order necessary conditions for an optimal reduced-order model. The H-2 norm representation also provides an error expression that motivates a new weighted-H-2 model reduction algorithm. Several numerical examples illustrate the effectiveness of the proposed approach. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Model reduction
Rational interpolation
Feedback control
Weighted model reduction
Weighted-H-2 approximation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W