Juš Kocijan

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Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process(More)
The magnitude optimum (MO) method provides a relatively fast and nonoscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection when controlling low-order processes. In this paper, the MO criterion is modified(More)
Different models can be used for nonlinear dynamic system identification and the Gaussian process model is a relatively new option with several interesting features: model predictions contain the measure of confidence, the model has a small number of training parameters and facilitated structure determination, and different possibilities of including prior(More)
Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gas emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the Nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion(More)
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process(More)
Gaussian process (GP) models are non-parametric, blackbox models that represent a new method for system identification. The optimization of GP models, due to their probabilistic nature, is based on maximization of the probability of the model. This probability can be calculated by the marginal likelihood. Commonly used approaches for maximizing the marginal(More)
Nonlinear Model Predictive Control (NMPC) algorithms are based on various nonlinear models. A number of on-line optimization approaches for outputfeedback NMPC based on various black-box models can be found in the literature. However, NMPC involving on-line optimization is computationally very demanding. On the other hand, an explicit solution to the NMPC(More)
On-line condition monitoring of rotational machinery is a very important part of modern control and supervision system. Various methods are used for dealing with this issue. This paper describes application of Gaussian process model for the modelling of time series describing gear health and the prediction of the critical value of harmonic component feature(More)
Control system based on evolving Gaussian process (GP) models is an example of self-learning closed-loop control system. It is meant for closed-loop control of dynamic systems where not much prior knowledge exists or where systems dynamics varies with time or operating region. GP models are non-parametric black-box models which represent a new method for(More)