Identification of Continuous-time Nonlinear Systems by Using a Gaussian Process Model

This paper deals with a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process model. Genetic algorithm is applied to train the Gaussian process prior model by minimizing the negative log marginal likelihood of the identification data. The nonlinear term of the objective system is estimated as the predictive mean function of the Gaussian process, and the confidence measure of the estimated nonlinear function is given by the predictive covariance function of the Gaussian process. (C) 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.