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Kocijan, J.; Murray-Smith, R.; Rasmussen, C.E.; Girard, A. (2004)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Languages: English
Types: Other
Subjects: TK
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 models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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