Sparse Bayesian Identification of Polynomial NARX Models. Issue 28 (2015)

• Record Type: Journal Article
• Authors: Jacobs, William R.; Baldacchino, Tara; Anderson, Sean R.
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 28(2015)
• Page Start: 172
• Page End: 177
• Publication Date: 2015
• Subjects: NARX models -- variational Bayes -- system identification -- automatic relevance determination; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.12.120
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper a novel sparse Bayesian structure detection algorithm is introduced for the identification of nonlinear autoregressive with exogenous inputs (NARX) dynamic systems. The main advantage of this algorithm over alternatives is that parameter uncertainty is naturally incorporated, and parameter estimation by variational inference is computationally efficient, consisting of a sequence of closed form updates. The proposed framework is demonstrated through a commonly used simulated benchmark problem.