Gaussian process based recursive system identification

European Workshop on Advanced Control and Diagnosis

Prüher, Jakub^1 ; Šimandl, Miroslav^1

University of West Bohemia, Czech Republic^1

关键词: Ad-hoc learning;    Computational burden;    Computational demands;    Gaussian process models;    Gaussian Processes;    Hyper-parameter;    Non-linear stochastic systems;    Non-linear system identification;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/570/1/012002/pdf
DOI  :  10.1088/1742-6596/570/1/012002

来源: IOP

【 摘 要 】

This paper is concerned with the problem of recursive system identification using nonparametric Gaussian process model. Non-linear stochastic system in consideration is affine in control and given in the input-output form. The use of recursive Gaussian process algorithm for non-linear system identification is proposed to alleviate the computational burden of full Gaussian process. The problem of an online hyper-parameter estimation is handled using proposed ad-hoc procedure. The approach to system identification using recursive Gaussian process is compared with full Gaussian process in terms of model error and uncertainty as well as computational demands. Using Monte Carlo simulations it is shown, that the use of recursive Gaussian process with an ad-hoc learning procedure offers converging estimates of hyper-parameters and constant computational demands.