System identification through online sparse Gaussian process regression with input noise. (31st December 2017)

• Record Type: Journal Article
• Authors: Bijl, Hildo; Schön, Thomas B.; van Wingerden, Jan-Willem; Verhaegen, Michel
• Is Part Of: IFAC journal of systems and control. Volume 2(2018)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-12-31
• Subjects: Nonlinear system identification -- Gaussian processes -- Regression -- Machine learning -- Sparse methods; Automatic control -- Periodicals; Relay control systems -- Periodicals; Embedded computer systems -- Periodicals; Feedback control systems -- Periodicals; Artificial intelligence -- Periodicals; Artificial intelligence; Automatic control; Embedded computer systems; Feedback control systems; Relay control systems; Electronic journals; Periodicals; 629.89
• DOI: 10.1016/j.ifacsc.2017.09.001
• Languages: English
• ISSNs: 2468-6018

Abstract

Abstract: There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and (3) it cannot deal with stochastic (noisy) input points. In this paper we present an algorithm tackling all these three issues simultaneously. The resulting Sparse Online Noisy Input GP (SONIG) regression algorithm can incorporate new noisy measurements in constant runtime. A comparison has shown that it is more accurate than similar existing regression algorithms. When applied to nonlinear black-box system modeling, its performance is competitive with existing nonlinear ARX models.