Sparse on-line Gaussian processes
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Explicit stochastic predictive control of combustion plants based on Gaussian process models
Automatica (Journal of IFAC)
Gaussian process dynamic programming
Neurocomputing
Analytic moment-based Gaussian process filtering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Optimization of gaussian process models with evolutionary algorithms
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Nonlinear predictive control with a gaussian process model
Switching and Learning in Feedback Systems
Gaussian process internal model control
International Journal of Systems Science
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Various methods can be used for nonlinear, dynamic-system identification and Gaussian process (GP) model is a relatively recent one. The GP model is an example of a probabilistic, nonparametric model with uncertainty predictions. It possesses several interesting features like model predictions contain the measure of confidence. Further, the model has a small number of training parameters, a facilitated structure determination and different possibilities of including prior knowledge about the modelled system. The framework for the identification of dynamic systems with GP models are presented and an overview of recent advances in the research of dynamic-system identification with GP models and its applications are given.