System Identification through Online Sparse Gaussian Process Regression with Input Noise
This addresses system identification problems for engineers and researchers by providing a more efficient and accurate regression method, though it appears incremental as it builds on existing sparse and online GP techniques.
The paper tackled the computational intensity, lack of online updating, and inability to handle noisy inputs in Gaussian Process regression for system identification, resulting in the SONIG algorithm that incorporates new noisy measurements in constant runtime and is more accurate than similar existing methods.
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.