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Special Issue Papers
Identification of continuous-time nonlinear systems by using a gaussian process model
Article first published online: 27 OCT 2008
DOI: 10.1002/tee.20323
Copyright © 2008 Institute of Electrical Engineers of Japan
Issue
1931-4981/asset/cover.gif?v=1&s=860ab1d8fea85def2538c42b51b552b530332094 "IEEJ Transactions on Electrical and Electronic Engineering")
IEEJ Transactions on Electrical and Electronic Engineering
Special Issue: Special Issue on Electronics, Information and Systems
Volume 3, Issue 6, pages 620–628, November 2008
Additional Information
How to Cite
Hachino, T. and Takata, H. (2008), Identification of continuous-time nonlinear systems by using a gaussian process model. IEEJ Transactions on Electrical and Electronic Engineering, 3: 620–628. doi: 10.1002/tee.20323
Publication History
- Issue published online: 27 OCT 2008
- Article first published online: 27 OCT 2008
- Manuscript Revised: 18 JUN 2008
- Manuscript Received: 7 FEB 2008
- Abstract
- References
- Cited By
Keywords:
- identification;
- continuous-time systems;
- nonlinear systems;
- Gaussian process model;
- genetic algorithm
Abstract
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. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.