Model reductions of high-order estimated models : the asymptotic ML approach
Model reductions of high-order estimated models : the asymptotic ML approach
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DOI：
10.1080/00207178908559628
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发表时间：
1989
影响因子：
2.1
通讯作者：
B. Wahlberg
中科院分区：
文献类型：
--
作者：
B. Wahlberg
从统计的观点讨论了由辨识实验得到的高阶模型的降阶问题。渐近最大似然（ML）的方法被定义为降低估计模型的阶数。该方法考虑了最大似然准则，给出了估计模型的渐近统计量（包括数据个数和阶数），并对应于频率加权L2-范数模型约简。利用渐近最大似然方法的思想，提出了一种基于高阶ARX估计和通过频率加权平衡实现的模型降阶的辨识算法。该算法的优点是不需要迭代最小化方法来找到估计。
Abstract The reduction of order of high-order models obtained from an identification experiment is discussed from a statistical point of view. The asymptotic maximum likelihood (ML) approach is defined to reduce the order of an estimated model. This approach considers the maximum likelihood criterion given the asymptotic statistics (both in the number of data and the order) of the estimated model, and corresponds to frequency weighted L 2-norm model reduction. By using the insight from the asymptotic ML approach, an identification algorithm is proposed based on a high- order ARX estimate and model reduction via a frequency weighted balanced realization. The advantage of this algorithm is that iterative minimization methods are not required to find the estimate.