文章基本信息

标题：Tuning the hyperparameters of the filter-based regularization method for impulse response estimation
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作者：Anna Marconato ; Maarten Schoukens
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：12841-12846
DOI：10.1016/j.ifacol.2017.08.1934
语种：English
出版社：Elsevier
摘要：AbstractThis paper discusses the use of a filter-based method for regularized impulse response modeling for linear time-invariant systems. The proposed method is a reformulation of the Bayesian, kernel based impulse response modeling approaches. The filter interpretation of the regularization cost function allows one to develop an intuitive framework to model a wide range of systems with different properties in a flexible way. Two hyperparameter selection techniques, based on Cross Validation and on Marginal Likelihood Maximization are presented. The proposed methods are tested on Monte Carlo simulation examples and on a real robotics problem. The results are compared with the ones obtained with the kernel-based methods based on the DC and TC kernels.