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Gaussian process (GP) probabilistic models have attractive advantages over parametric and neural network modeling approaches. They have a small number of tuneable parameters, can be trained on relatively small training sets, and provide a measure of prediction certainty. In this paper, these properties are exploited to develop two methods of highlighting(More)
A Bayesian Gaussian process (GP) modeling approach has recently been introduced to model-based control strategies. The estimate of the variance of the predicted output is the most useful advantage of GPs in comparison to neural networks (NNs) and fuzzy models. However, the GP model is computationally demanding and nontransparent. To reduce the computation(More)
— To improve transparency and reduce the curse of di-mensionality of non–linear black–box models, the local modelling approach was proposed. Poor transient response of Local Model networks led to the use of non–parametrical probabilistic models such as the Gaussian Process prior approach. Recently, Gaussian Process models were applied in the Minimum(More)
— Parametric multiple model techniques have recently been proposed for the modelling of non–linear systems and use in nonlinear control. Research effort has focused on issues such as the selection of the structure, constructive learning techniques, computational issues, the curse of dimensionality, off–equilibrium behavior etc. To reduce these problems, the(More)
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