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Gaussian Process Model Identification Using Artificial Bee Colony Algorithm and Its Application to Modeling of Power Systems

Authors: Hitoshi Takata, Tomohiro Hachino, Shigeru Nakayama, Ichiro Iimura, Seiji Fukushima, Yasutaka Igarashi


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.

Keywords: Identification, Electric Power System, Nonlinear System, Artificial Bee Colony Algorithm, Gaussian process model

Digital Object Identifier (DOI):

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