Structured low-rank approximation and its applications

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• 作者：Ivan Markovsky
• 关键词：Low-rank approximation ; Total least squares ; System identification ; Errors-in-variables modeling ; Behaviors
• 刊名：Automatica
• 出版年：2008
• 出版时间：April 2008
• 年：2008
• 卷：44
• 期：4
• 页码：891-909
• 全文大小：364 K

文摘

Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data and the rank constraint is related to a bound on the model complexity. In the special case of fitting by a static model, the data matrix and its low-rank approximation are unstructured.