  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 non-linear black-box system modeling, its performance is competitive
with existing non-linear ARX models.
