文章基本信息

标题：Regularized LTI System Identification with Multiple Regularization Matrix ⁎
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作者：Tianshi Chen ; Martin S. Andersen ; Biqiang Mu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：180-185
DOI：10.1016/j.ifacol.2018.09.121
语种：English
出版社：Elsevier
摘要：AbstractRegularization methods with regularization matrix in quadratic form have received increasing attention. For those methods, the design and tuning of the regularization matrix are two key issues that are closely related. For systems with complicated dynamics, it would be preferable that the designed regularization matrix can bring the hyper-parameter estimation problem certain structure such that a locally optimal solution can be found efficiently. An example of this idea is to use the so-called multiple kernel Chen et al. (2014) for kernel-based regularization methods. In this paper, we propose to use the multiple regularization matrix for the filter-based regularization. Interestingly, the marginal likelihood maximization with the multiple regularization matrix is also a difference of convex programming problem, and a locally optimal solution could be found with sequential convex optimization techniques.
关键词：KeywordsSystem identificationregularization methodssequential convex optimization