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1 2008

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Gaussian process model identification: a process engineering case study

Kocijan J., Azman K.

Systems Science

2008

Vol. 34, no 3

31-38

Gaussian process prior models, although well known in the Bayesian statistics community, are a relatively new approach for the modelling of dynamic systems and consequently a novelty in systems and control community. The Gaussian process prior model is a probabilistic nonparametric model. The complexity of such a model depends on the amount of input data used for identification and contained in the model. This complexity adds to the computational load necessary for multiple-step-ahead prediction and for model simulation. The combination of local linear models and sparse data in off-equilibrium regions can be utilised to reduce the amount of data in Gaussian process model, which is in accord with engineering practice where the situation with lots of measured data in equilibrium regions and sparse measured data in off-equilibrium regions is frequently faced. This approach is used in the paper for the modelling of a gas-liquid separator process plant, where substantial amount of measurement data in equilibrium region and sparse measurement data in distance from equilibrium region are combined in one Gaussian process model.