基于系统矩阵实Schur分解的集结法模型降阶
Aggregation Model Reduction Based on Real Schur Decomposition of System Matrix
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摘要: 通过有序实Schur分解将系统矩阵变成分块对角阵,得到一种数值稳定的集结法模型降 阶,并给出降阶的L∞-误差界.降价系统保留了原系统的主导极点且为最小实现.Abstract: In this paper, by transforming the system matrix into a blocked diagonal matrix, it obtains an aggregation model reduction algorithm with numerical stability, and gives a L∞-error bound for model reduction. The reduced-order system retains the prominent poles and its realization is minimal.
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Key words:
- Model reduction /
- aggregation method /
- real Schur decomposition
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