%0 Journal Article
	%A Tomohiro Hachino and  Hitoshi Takata and  Shigeru Nakayama and  Ichiro Iimura and  Seiji Fukushima and  Yasutaka Igarashi
	%D 2014
	%J International Journal of Electrical and Computer Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 86, 2014
	%T Gaussian Process Model Identification Using Artificial Bee Colony Algorithm and Its Application to Modeling of Power Systems
	%U https://publications.waset.org/pdf/9998340
	%V 86
	%X This paper presents a nonparametric identification of
continuous-time nonlinear systems by using a Gaussian process
(GP) model. The GP prior model is trained by artificial bee colony
algorithm. The nonlinear function of the objective system is estimated
as the predictive mean function of the GP, and the confidence
measure of the estimated nonlinear function is given by the predictive
covariance of the GP. The proposed identification method is applied
to modeling of a simplified electric power system. Simulation results
are shown to demonstrate the effectiveness of the proposed method.

	%P 441 - 446