The role of rank penalties in linear system identification. Issue 28 (2015)

• Record Type: Journal Article
• Authors: Prando, G.; Pillonetto, G.; Chiuso, A.
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 28(2015)
• Page Start: 1293
• Page End: 1300
• Publication Date: 2015
• Subjects: Identification -- Learning -- Numerical Methods -- Linear Systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.12.310
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We discuss the linear system identification methods that are based on a regularized estimation problem including a rank penalty (typically formulated in terms of nuclear norm).We provide a common framework, under which most of these procedures can be recast. Following the Bayesian approach to system identification, we also introduce a Gaussian prior inducing a rank penalty and we prove the effectiveness of this method through a Monte-Carlo experiment.