Regularized LTI System Identification with Multiple Regularization Matrix

Tianshi CHEN, Martin S. ANDERSEN, Biqiang MU, Feng YIN, Lennart LJUNG, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)


Regularization 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.
Original languageEnglish
Pages (from-to)180-185
Number of pages6
Issue number15
Early online date8 Oct 2018
Publication statusPublished - 2018
Externally publishedYes


  • regularization methods
  • sequential convex optimization
  • System identification


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