Abstract
The article deals with the issues of handling uncertainties in the problems of modeling and forecasting dynamic systems within the framework of the dynamic planning methodology. To analyze and take into account possible structural, statistical and parametric uncertainties, the Kalman filter, various methods for calculating missing data, numerous methods for estimating the model parameters and the Bayesian approach to programming are used. The questions of an estimation of quality of predicted decisions are considered.
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Bidyuk, P., Gozhyj, A., Kalinina, I., Gozhyj, V. (2018). Analysis of Uncertainty Types for Model Building and Forecasting Dynamic Processes. In: Shakhovska, N., Stepashko, V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-70581-1_5
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DOI: https://doi.org/10.1007/978-3-319-70581-1_5
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