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.
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Hachino, T., Yamakawa, S. Non-parametric identification of continuous-time Hammerstein systems using Gaussian process model and particle swarm optimization. Artif Life Robotics 17, 35–40 (2012). https://doi.org/10.1007/s10015-012-0015-7
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DOI: https://doi.org/10.1007/s10015-012-0015-7