Non-parametric identification of continuous-time Hammerstein systems using Gaussian process model and particle swarm optimization

Authors:
Tomohiro Hachino;Shoichi Yamakawa
Affiliations:
Department of Electrical and Electronics Engineering, Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan 890-0065;Department of Electrical and Electronics Engineering, Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan 890-0065
Venue:
Artificial Life and Robotics
Year:
2012

Citing 2
Cited 0

A new method for the identification of Hammerstein model

Automatica (Journal of IFAC)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

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Abstract

This paper deals with a non-parametric identification of continuous-time Hammerstein systems using Gaussian process (GP) models. A Hammerstein system consists of a memoryless non-linear static part followed by a linear dynamic part. The identification model is derived using the GP prior model which is described by the mean function vector and the covariance matrix. This prior model is trained by the separable least-squares (LS) approach combining the linear LS method with particle swarm optimization to minimize the negative log marginal likelihood of the identification data. Then the non-linear static part is estimated by the predictive mean function of the GP, and the confidence measure of the estimated non-linear static part is evaluated by the predictive covariance function of the GP. Simulation results are shown to illustrate the proposed method.