Multiple Kernel Based Regularized System Identification with SURE Hyper-parameter Estimator

Shiying Hong, Biqiang Mu, Feng Yin, Martin S. Andersen, Tianshi Chen

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In this work, we study the multiple kernel based regularized system identification with the hyper-parameter estimated by using the Stein's unbiased risk estimators (SURE). To approach the problem, a QR factorization is first employed to compute SURE's objective function and its gradient in an efficient and accurate way. Then we propose an algorithm to solve the SURE problem, which contains two parts: the outer optimization part and the inner optimization part. For the outer optimization part, the coordinate descent algorithm is used and for the inner optimization part, the projection gradient algorithm is used. Finally, the efficacy of the proposed algorithm is demonstrated by numerical simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Original languageEnglish
Book seriesI F A C Workshop Series
Issue number15
Pages (from-to)13-18
Publication statusPublished - 2018


  • Linear system identification
  • regularization methods
  • hyper-parameter estimation
  • SURE
  • multiple kernel

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