System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques

Tianshi Chen, Martin Skovgaard Andersen, Lennart Ljung, Alessandro Chiuso, Gianluigi Pillonetto

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels suitable for impulse response estimation, and equip the kernel-based regularization method with three features. First, multiple kernels can better capture complicated dynamics than single kernels. Second, the estimation of their weights by maximizing the marginal likelihood favors sparse optimal weights, which enables this method to tackle various structure detection problems, e.g., the sparse dynamic network identification and the segmentation of linear systems. Third, the marginal likelihood maximization problem is a difference of convex programming problem. It is thus possible to find a locally optimal solution efficiently by using a majorization minimization algorithm and an interior point method where the cost of a single interior-point iteration grows linearly in the number of fixed kernels. Monte Carlo simulations show that the locally optimal solutions lead to good performance for randomly generated starting points.
Original languageEnglish
JournalI E E E Transactions on Automatic Control
Volume59
Issue number11
Pages (from-to)2933-2945
ISSN0018-9286
DOIs
Publication statusPublished - 2014

Keywords

  • System identification
  • regularization methods
  • kernel methods
  • convex optimization
  • sparsity
  • structure detection
  • kernel

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