Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm
Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm
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DOI：
10.1080/00207179208934364
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发表时间：
1992-11
影响因子：
2.1
通讯作者：
M. Verhaegen
中科院分区：
文献类型：
--
作者：
M. Verhaegen
本文分析了本系列论文第一部分提出的基本MOESP算法。这是通过三种不同的方式完成的。首先，我们研究了仅考虑输出序列零均值白色噪声扰动时状态空间模型估计的渐近性质。它表明，在这种情况下，MOESPl实现产生渐近无偏估计。这个结果的一个重要约束是，底层系统必须有一个有限的脉冲响应和随后的汉克尔矩阵的大小，从输入和输出数据在开始的计算，取决于非零马尔可夫参数的数量。然而，这种分析导致了基本MOESP方案的第二种实现，即MOESP 2。后一种实现方式具有相同的渐近性质，没有有限脉冲响应约束。其次，我们比较了MOESP2算法与经典的状态空间模型辨识方案。后者...
The elementary MOESP algorithm presented in the first part of this series of papers is analysed in this paper. This is done in three different ways. First, we study the asymptotic properties of the estimated state-space model when only considering zero-mean white noise perturbations on the output sequence. It is shown that, in this case, the MOESPl implementation yields asymptotically unbiased estimates. An important constraint to this result is that the underlying system must have a finite impulse response and subsequently the size of the Hankel matrices, constructed from the input and output data at the beginning of the computations, depends on the number of non-zero Markov parameters. This analysis, however, leads to a second implementation of the elementary MOESP scheme, namely MOESP2. The latter implementation has the same asymptotic properties without the finite impulse response constraint. Secondly, we compare the MOESP2 algorithm with a classical state space model identification scheme. The latter...