文章基本信息

标题：Comparison of Regularized Least-Squares Approach and Predictive Error Method: Case Studies
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作者：Mengyuan Fang ; Yucai Zhu
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：3989-3994
DOI：10.1016/j.ifacol.2017.08.772
语种：English
出版社：Elsevier
摘要：AbstractThere are recently studies on linear system identification with high order finite impulse response (FIR) models using the regularized least-squares approach. The regularized approach allows some bias to reduce variance so that the error of the regularized FIR model is minimized. This paper concerns the comparison between the regularized least-square approach and the traditional predictive error method (PEM), using a number of case studies. More cases are included than those used by the authors of the regularized approach, for example, non-white input signal, non-white output noise, low-order systems and closed-loop tests. In most cases PEM outperforms the regularized approach.
关键词：Keywordsregularizationpredictive error methodbias-variance trade-offasymptotic method