Low-order model identification of MIMO systems from noisy and incomplete data
Low-order model identification of MIMO systems from noisy and incomplete data
复制标题
从噪声和不完整数据中识别 MIMO 系统的低阶模型
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Sznaier
中科院分区：
文献类型：
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作者：
K. Bekiroglu;C. Lagoa;M. Sznaier
在本文中，我们提供了初步的结果，旨在解决以下问题：给定先验信息的多输入/多输出（MIMO）的工厂，即对极点位置的约束，和分散的输入/输出数据，找到最低阶模型，这是兼容的先验假设和收集的数据。通过结合信号稀疏化和子空间识别的概念，开发了可以从被测量噪声破坏并且具有丢失测量的数据确定低阶模型的算法。最后通过一个算例验证了该方法的有效性。
In this paper, we provide preliminary results aimed at solving the following problem: Given a priori information on Multi-Input/Multi-Output (MIMO) plant, namely constraints on the pole location, and scattered input/output data, find the lowest order model that is compatible with both the a priori assumptions and the collected data. By combining concepts from signal sparsification and subspace identification, algorithms are developed that can determine a low order model from data that is both corrupted by measurement noise and has missing measurements. Effectiveness of the proposed approach is shown by an academic example.