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

被引：2

作者：

Hachino, Tomohiro ^{[1
]}

Takata, Hitoshi ^{[1
]}

机构：

[1] Kagoshima Univ, Dept Elect & Elect Engn, Kagoshima 8900065, Japan

来源：

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING | 2008年 / 3卷 / 06期

关键词：

identification; continuous-time systems; nonlinear systems; Gaussian process model; genetic algorithm;

D O I：

10.1002/tee.20323

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：620 / 628

页数：9

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