文章基本信息

标题：Low-Complexity Identification by Sparse Hyperparameter Estimation ⁎
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作者：Mohammad Khosravi ; Mingzhou Yin ; Andrea Iannelli 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：412-417
DOI：10.1016/j.ifacol.2020.12.207
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents a novel kernel-based system identification method, which promotes low complexity of the model in terms of the McMillan degree of the system. The regularization matrix is characterized as a linear combination of pre-selected rank-one matrices with unknown hyperparameter coefficients, and the hyperparameters are derived using a maximuma posterioriestimation approach. Each basis matrix is the optimal regularization matrix for a first-order system. With this basis matrix selection, the McMillan degree of the identified model is upper-bounded by the rank of the regularization matrix, which in turn is equal to the cardinality of the hyperparameters. For this reason, a sparsity-promoting prior is chosen for hyperparameter tuning. The resulting optimization problem has a difference of convex program form which can be efficiently solved. The advantages of the proposed method are that the identified model has a low-complexity structure and that an improved bias-variance trade-off is achieved. Numerical results confirm that the proposed method achieves a better bias-variance trade-off as well as a better fit to the model compared to both the empirical Bayes method and the atomic-norm regularization.
关键词：KeywordsSystem identificationregularizationcomplexity tuninghyperparameter estimation