A comparative study of 7 algorithms for model reduction
A comparative study of 7 algorithms for model reduction
复制标题
7种模型降维算法的比较研究
DOI：
10.1109/cdc.2000.914153
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
A. Antoulas
中科院分区：
文献类型：
--
作者：
S. Gugercin;A. Antoulas
比较七个模型降阶算法，将它们应用到四个不同的动力系统。有四种基于奇异值分解（SVD）的方法，三种基于矩匹配的方法。结果表明，在考虑整个频率范围时，总体均衡降阶和近似均衡降阶效果最好。矩匹配方法由于其局部性质总是导致比基于SVD的方法更高的误差范数，但它们在数值上更有效。其中，理性Krylov算法给出了最好的结果。
Compares seven model reduction algorithms by applying them to four different dynamical systems. There are four singular value decomposition (SVD) based methods, and three moment matching based methods. The results illustrate that overall, balanced reduction and approximate balanced reduction are the best when we consider whole frequency range. Moment matching methods always lead to higher error norms than SVD based methods due to their local nature; but they are numerically more efficient. Among them, the rational Krylov algorithm gives the best results.