A convex optimization approach to semi-supervised identification of switched ARX systems
A convex optimization approach to semi-supervised identification of switched ARX systems
复制标题
切换 ARX 系统半监督识别的凸优化方法
DOI：
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发表时间：
2014
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：
Yongfang Cheng;Yin Wang;M. Sznaier
本文提出了一个通用的凸框架，用于从被噪声（过程和测量）和离群值污染的测量中鲁棒地识别离散时间仿射混合系统。我们的主要结果表明，这个问题可以制定为一个约束多项式优化，其中单调收敛序列的易处理的凸松弛可以利用最近的发展稀疏多项式优化。所提出的框架的一个显着特点是它能够将现有的先验信息的噪声，co-ocurrency，或切换序列。这些结果与几个例子说明了所提出的方法，使有效地利用这些额外的信息的能力。
This paper proposes a general convex framework for robustly identifying discrete-time affine hybrid systems from measurements contaminated by noise (both process and measurement) and outliers. Our main result shows that this problem can be formulated as a constrained polynomial optimization, for which a monotonically convergent sequence of tractable convex relaxations can be obtained by exploiting recent developments in sparse polynomial optimization. A salient feature of the proposed framework is its ability to incorporate existing a-priori information about the noise, co-ocurrences, or the switching sequence. These results are illustrated with several examples showing the ability of the proposed approach to make effective use of this additional information.