Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

Abstract

This paper deals with a nonparametric identification of continuous-time nonlinear systems using multiple local Gaussian process (GP) models. Multiple sets of training input and output data are collected to train the local GP prior models. Each local GP prior model is trained by minimizing the negative log marginal likelihood of each set of the training data. The final nonlinear function with confidence measure is estimated by weighted mean of the local estimated nonlinear functions using the predictive variances of local GP posterior distributions. Compared to the standard GP-based identification method, the proposed method can reduce the computational cost and improve the accuracy of identification. Simulation results are shown to illustrate the effectiveness of the proposed identification method.