Exact system identification with missing data
Exact system identification with missing data
复制标题
精确识别缺失数据的系统
DOI：
10.1109/cdc.2013.6759874
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
I. Markovsky
中科院分区：
文献类型：
--
作者：
I. Markovsky
本文提出了一个子空间方法精确识别的线性时不变系统的缺失值的数据的初步结果。缺失数据的辨识问题等价于一个Hankel结构的低秩矩阵完备化问题。新的思想是系统地搜索和有效地使用完全指定的子矩阵的不完全汉克尔矩阵构造从给定的数据。亏秩完全指定子矩阵的非平凡核携带有关待识别系统的信息。将这些信息组合到所识别系统的完整模型中是最大公约数计算问题。所开发的子空间方法具有线性的计算复杂性的数据点的数量，因此是一个有吸引力的替代更昂贵的方法的基础上的核范数启发式。
The paper presents initial results on a subspace method for exact identification of a linear time-invariant system from data with missing values. The identification problem with missing data is equivalent to a Hankel structured low-rank matrix completion problem. The novel idea is to search systematically and use effectively completely specified submatrices of the incomplete Hankel matrix constructed from the given data. Nontrivial kernels of the rank-deficient completely specified submatrices carry information about the to-be-identified system. Combining this information into a full model of the identified system is a greatest common divisor computation problem. The developed subspace method has linear computational complexity in the number of data points and is therefore an attractive alternative to more expensive methods based on the nuclear norm heuristic.