Data-Augmented Numerical Integration in State Prediction: Rule Selection

This paper deals with the state prediction of nonlinear stochastic dynamic systems. The emphasis is laid on a solution to the integral Chapman-Kolmogorov equation by a deterministic-integration-rule-based point-mass method. A novel concept of reliable data-augmented, i.e., mathematics- and data-info...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2024, Vol.58 (15), p.139-144
Main Authors: Duník, J., Král, L., Matoušek, J., Straka, O., Brandner, M.
Format: Article
Language:English
Subjects:
Bayesian methods
Neural network
Nonlinear predictors
Numerical integration
State estimation
Stochastic systems
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Summary:This paper deals with the state prediction of nonlinear stochastic dynamic systems. The emphasis is laid on a solution to the integral Chapman-Kolmogorov equation by a deterministic-integration-rule-based point-mass method. A novel concept of reliable data-augmented, i.e., mathematics- and data-informed, integration rule is developed to enhance the point-mass state predictor, where the trained neural network (representing data contribution) is used for the selection of the best integration rule from a set of available rules (representing mathematics contribution). The proposed approach combining the best properties of the standard mathematics-informed and novel data-informed rules is thoroughly discussed.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2024.08.518