Multiple Kernel Based Regularized System Identification with SURE Hyper-parameter Estimator. Issue 15 (2018)

• Record Type: Journal Article
• Authors: Hong, Shiying; Mu, Biqiang; Yin, Feng; Andersen, Martin S.; Chen, Tianshi
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 15(2018)
• Page Start: 13
• Page End: 18
• Publication Date: 2018
• Subjects: Linear system identification -- regularization methods -- hyper-parameter estimation -- SURE -- multiple kernel; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.083
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.